diff --git a/automotive/can/1.0/default/libnl++/Android.bp b/automotive/can/1.0/default/libnl++/Android.bp
index a69e302..9e18ba0 100644
--- a/automotive/can/1.0/default/libnl++/Android.bp
+++ b/automotive/can/1.0/default/libnl++/Android.bp
@@ -26,6 +26,7 @@
         "protocols/generic/Generic.cpp",
         "protocols/generic/GenericMessageBase.cpp",
         "protocols/generic/Unknown.cpp",
+        "protocols/generic/families/Mac80211hwsim.cpp",
         "protocols/generic/families/Nl80211.cpp",
         "protocols/route/Link.cpp",
         "protocols/route/Route.cpp",
@@ -33,6 +34,7 @@
         "protocols/MessageDefinition.cpp",
         "protocols/NetlinkProtocol.cpp",
         "protocols/all.cpp",
+        "protocols/structs.cpp",
         "Attributes.cpp",
         "MessageFactory.cpp",
         "MessageMutator.cpp",
diff --git a/automotive/can/1.0/default/libnl++/MessageMutator.cpp b/automotive/can/1.0/default/libnl++/MessageMutator.cpp
index 00b48a6..de2a2b1 100644
--- a/automotive/can/1.0/default/libnl++/MessageMutator.cpp
+++ b/automotive/can/1.0/default/libnl++/MessageMutator.cpp
@@ -19,7 +19,7 @@
 namespace android::nl {
 
 MessageMutator::MessageMutator(nlmsghdr* buffer, size_t totalLen)
-    : mConstBuffer(buffer, totalLen), mMutableBuffer(buffer) {
+    : mMutableBuffer(buffer), mTotalLen(totalLen) {
     CHECK(totalLen >= sizeof(nlmsghdr));
 }
 
@@ -27,8 +27,12 @@
     return mMutableBuffer;
 }
 
+Buffer<nlmsghdr> MessageMutator::constBuffer() const {
+    return {mMutableBuffer, mTotalLen};
+}
+
 MessageMutator::operator Buffer<nlmsghdr>() const {
-    return mConstBuffer;
+    return constBuffer();
 }
 
 uint64_t MessageMutator::read(Buffer<nlattr> attr) const {
@@ -37,7 +41,8 @@
 
 void MessageMutator::write(Buffer<nlattr> attr, uint64_t val) const {
     const auto attrData = attr.data<uint64_t>();
-    const auto offset = mConstBuffer.getOffset(attrData);
+    // TODO(b/177251183): deduplicate this code against fragment()
+    const auto offset = constBuffer().getOffset(attrData);
     CHECK(offset.has_value()) << "Trying to write attribute that's not a member of this message";
 
     const auto writeableBuffer = reinterpret_cast<uint8_t*>(mMutableBuffer) + *offset;
@@ -47,4 +52,40 @@
     memcpy(writeableBuffer, &val, std::min(sizeof(val), attrSize));
 }
 
+MessageMutator MessageMutator::fragment(Buffer<nlmsghdr> buf) const {
+    const auto offset = constBuffer().getOffset(buf);
+    CHECK(offset.has_value()) << "Trying to modify a fragment outside of buffer range";
+
+    const auto writeableBuffer = reinterpret_cast<nlmsghdr*>(uintptr_t(mMutableBuffer) + *offset);
+    const auto len = buf.getRaw().len();
+    CHECK(len <= mTotalLen - *offset);
+
+    return {writeableBuffer, len};
+}
+
+MessageMutator::iterator MessageMutator::begin() const {
+    return {*this, constBuffer().begin()};
+}
+
+MessageMutator::iterator MessageMutator::end() const {
+    return {*this, constBuffer().end()};
+}
+
+MessageMutator::iterator::iterator(const MessageMutator& container,
+                                   Buffer<nlmsghdr>::iterator current)
+    : mContainer(container), mCurrent(current) {}
+
+MessageMutator::iterator MessageMutator::iterator::operator++() {
+    ++mCurrent;
+    return *this;
+}
+
+bool MessageMutator::iterator::operator==(const iterator& other) const {
+    return other.mCurrent == mCurrent;
+}
+
+const MessageMutator MessageMutator::iterator::operator*() const {
+    return mContainer.fragment(*mCurrent);
+}
+
 }  // namespace android::nl
diff --git a/automotive/can/1.0/default/libnl++/include/libnl++/Buffer.h b/automotive/can/1.0/default/libnl++/include/libnl++/Buffer.h
index d759a0a..4cabb9a 100644
--- a/automotive/can/1.0/default/libnl++/include/libnl++/Buffer.h
+++ b/automotive/can/1.0/default/libnl++/include/libnl++/Buffer.h
@@ -138,7 +138,7 @@
     class raw_iterator : public iterator {
       public:
         iterator operator++() {
-            this->mCurrent.mData++;  // ignore alignment
+            ++this->mCurrent.mData;  // ignore alignment
             return *this;
         }
         const T& operator*() const { return *this->mCurrent.mData; }
diff --git a/automotive/can/1.0/default/libnl++/include/libnl++/Message.h b/automotive/can/1.0/default/libnl++/include/libnl++/Message.h
index 50b3c4b..29f397d 100644
--- a/automotive/can/1.0/default/libnl++/include/libnl++/Message.h
+++ b/automotive/can/1.0/default/libnl++/include/libnl++/Message.h
@@ -34,7 +34,7 @@
  * a single instance can only be used by a single thread - the one owning the underlying buffer).
  */
 template <typename T>
-class Message {
+class Message : public Buffer<nlmsghdr> {
   public:
     /**
      * Validate buffer contents as a message carrying T data and create instance of parsed message.
@@ -51,7 +51,7 @@
 
         const auto attributes = buf.data<nlattr>(sizeof(T));
 
-        return Message<T>(nlHeader, dataHeader, attributes);
+        return Message<T>(buf, nlHeader, dataHeader, attributes);
     }
 
     /**
@@ -94,8 +94,9 @@
     const T* operator->() const { return &data; }
 
   private:
-    Message(const nlmsghdr& nlHeader, const T& dataHeader, Attributes attributes)
-        : header(nlHeader), data(dataHeader), attributes(attributes) {}
+    Message(Buffer<nlmsghdr> buffer, const nlmsghdr& nlHeader, const T& dataHeader,
+            Attributes attributes)
+        : Buffer<nlmsghdr>(buffer), header(nlHeader), data(dataHeader), attributes(attributes) {}
 };
 
 }  // namespace android::nl
diff --git a/automotive/can/1.0/default/libnl++/include/libnl++/MessageMutator.h b/automotive/can/1.0/default/libnl++/include/libnl++/MessageMutator.h
index 7d495e9..baadc44 100644
--- a/automotive/can/1.0/default/libnl++/include/libnl++/MessageMutator.h
+++ b/automotive/can/1.0/default/libnl++/include/libnl++/MessageMutator.h
@@ -53,9 +53,27 @@
      */
     void write(Buffer<nlattr> attr, uint64_t val) const;
 
+    class iterator {
+      public:
+        iterator(const MessageMutator& container, Buffer<nlmsghdr>::iterator current);
+
+        iterator operator++();
+        bool operator==(const iterator& other) const;
+        const MessageMutator operator*() const;
+
+      protected:
+        const MessageMutator& mContainer;
+        Buffer<nlmsghdr>::iterator mCurrent;
+    };
+    iterator begin() const;
+    iterator end() const;
+
   private:
-    const Buffer<nlmsghdr> mConstBuffer;
     nlmsghdr* mMutableBuffer;
+    size_t mTotalLen;
+
+    Buffer<nlmsghdr> constBuffer() const;
+    MessageMutator fragment(Buffer<nlmsghdr> buf) const;
 };
 
 }  // namespace android::nl
diff --git a/automotive/can/1.0/default/libnl++/include/libnl++/generic/families/mac80211_hwsim.h b/automotive/can/1.0/default/libnl++/include/libnl++/generic/families/mac80211_hwsim.h
new file mode 100644
index 0000000..9b811f8
--- /dev/null
+++ b/automotive/can/1.0/default/libnl++/include/libnl++/generic/families/mac80211_hwsim.h
@@ -0,0 +1,73 @@
+/*
+ * Copyright (C) 2021 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// API definitions from kernel drivers/net/wireless/mac80211_hwsim.h
+
+#define BIT(n) (1 << (n))
+
+enum hwsim_tx_control_flags {
+    HWSIM_TX_CTL_REQ_TX_STATUS = BIT(0),
+    HWSIM_TX_CTL_NO_ACK = BIT(1),
+    HWSIM_TX_STAT_ACK = BIT(2),
+};
+
+enum {
+    HWSIM_CMD_UNSPEC,
+    HWSIM_CMD_REGISTER,
+    HWSIM_CMD_FRAME,
+    HWSIM_CMD_TX_INFO_FRAME,
+    HWSIM_CMD_NEW_RADIO,
+    HWSIM_CMD_DEL_RADIO,
+    HWSIM_CMD_GET_RADIO,
+    HWSIM_CMD_ADD_MAC_ADDR,
+    HWSIM_CMD_DEL_MAC_ADDR,
+};
+
+enum {
+    HWSIM_ATTR_UNSPEC,
+    HWSIM_ATTR_ADDR_RECEIVER,
+    HWSIM_ATTR_ADDR_TRANSMITTER,
+    HWSIM_ATTR_FRAME,
+    HWSIM_ATTR_FLAGS,
+    HWSIM_ATTR_RX_RATE,
+    HWSIM_ATTR_SIGNAL,
+    HWSIM_ATTR_TX_INFO,
+    HWSIM_ATTR_COOKIE,
+    HWSIM_ATTR_CHANNELS,
+    HWSIM_ATTR_RADIO_ID,
+    HWSIM_ATTR_REG_HINT_ALPHA2,
+    HWSIM_ATTR_REG_CUSTOM_REG,
+    HWSIM_ATTR_REG_STRICT_REG,
+    HWSIM_ATTR_SUPPORT_P2P_DEVICE,
+    HWSIM_ATTR_USE_CHANCTX,
+    HWSIM_ATTR_DESTROY_RADIO_ON_CLOSE,
+    HWSIM_ATTR_RADIO_NAME,
+    HWSIM_ATTR_NO_VIF,
+    HWSIM_ATTR_FREQ,
+    HWSIM_ATTR_PAD,
+    HWSIM_ATTR_TX_INFO_FLAGS,
+    HWSIM_ATTR_PERM_ADDR,
+    HWSIM_ATTR_IFTYPE_SUPPORT,
+    HWSIM_ATTR_CIPHER_SUPPORT,
+};
+
+struct hwsim_tx_rate {
+    int8_t idx;
+    uint8_t count;
+} __packed;
+static_assert(sizeof(hwsim_tx_rate) == 2);
+
+#undef BIT
diff --git a/automotive/can/1.0/default/libnl++/printer.cpp b/automotive/can/1.0/default/libnl++/printer.cpp
index f08897e..d540482 100644
--- a/automotive/can/1.0/default/libnl++/printer.cpp
+++ b/automotive/can/1.0/default/libnl++/printer.cpp
@@ -154,16 +154,19 @@
     }
 }
 
-std::string toString(const Buffer<nlmsghdr> hdr, int protocol, bool printPayload) {
-    if (!hdr.firstOk()) return "nlmsg{buffer overflow}";
+static void toStream(std::stringstream& ss, const Buffer<nlmsghdr> hdr, int protocol,
+                     bool printPayload) {
+    if (!hdr.firstOk()) {
+        ss << "nlmsg{buffer overflow}";
+        return;
+    }
 
-    std::stringstream ss;
     ss << std::setfill('0');
 
     auto protocolMaybe = protocols::get(protocol);
     if (!protocolMaybe.has_value()) {
         ss << "nlmsg{protocol=" << protocol << "}";
-        return ss.str();
+        return;
     }
     protocols::NetlinkProtocol& protocolDescr = *protocolMaybe;
 
@@ -187,7 +190,7 @@
     ss << ", crc=" << std::hex << std::setw(4) << crc16(hdr.data<uint8_t>()) << std::dec;
     ss << '}';
 
-    if (!printPayload) return ss.str();
+    if (!printPayload) return;
     ss << ' ';
 
     if (!msgDescMaybe.has_value()) {
@@ -210,6 +213,17 @@
     }
 
     ss << "}";
+}
+
+std::string toString(const Buffer<nlmsghdr> hdrs, int protocol, bool printPayload) {
+    std::stringstream ss;
+    bool first = true;
+    for (const auto hdr : hdrs) {
+        if (!first) ss << std::endl;
+        first = false;
+
+        toStream(ss, hdr, protocol, printPayload);
+    }
 
     return ss.str();
 }
diff --git a/automotive/can/1.0/default/libnl++/protocols/generic/Ctrl.cpp b/automotive/can/1.0/default/libnl++/protocols/generic/Ctrl.cpp
index 1e1ad12..478c383 100644
--- a/automotive/can/1.0/default/libnl++/protocols/generic/Ctrl.cpp
+++ b/automotive/can/1.0/default/libnl++/protocols/generic/Ctrl.cpp
@@ -16,6 +16,7 @@
 
 #include "Ctrl.h"
 
+#include "families/Mac80211hwsim.h"
 #include "families/Nl80211.h"
 
 #include <libnl++/Message.h>
@@ -68,12 +69,15 @@
     const auto familyId = msg.attributes.get<uint16_t>(CTRL_ATTR_FAMILY_ID);
     const auto familyName = msg.attributes.get<std::string>(CTRL_ATTR_FAMILY_NAME);
 
-    /* For now, we support just a single family. But if you add more, please define proper
+    /* For now, we support just two families. But if you add more, please define proper
      * abstraction and not hardcode every name and class here.
      */
     if (familyName == "nl80211") {
         mFamilyRegister[familyId] = std::make_shared<families::Nl80211>(familyId);
     }
+    if (familyName == "MAC80211_HWSIM") {
+        mFamilyRegister[familyId] = std::make_shared<families::Mac80211hwsim>(familyId);
+    }
 }
 
 }  // namespace android::nl::protocols::generic
diff --git a/automotive/can/1.0/default/libnl++/protocols/generic/GenericMessageBase.cpp b/automotive/can/1.0/default/libnl++/protocols/generic/GenericMessageBase.cpp
index b7b811b..f92d6c0 100644
--- a/automotive/can/1.0/default/libnl++/protocols/generic/GenericMessageBase.cpp
+++ b/automotive/can/1.0/default/libnl++/protocols/generic/GenericMessageBase.cpp
@@ -40,9 +40,9 @@
 
     ss << "genlmsghdr{";
     if (commandName.has_value()) {
-        ss << "cmd=" << unsigned(data.cmd);
-    } else {
         ss << "cmd=" << *commandName;
+    } else {
+        ss << "cmd=" << unsigned(data.cmd);
     }
     ss << ", version=" << unsigned(data.version);
     if (data.reserved != 0) ss << ", reserved=" << data.reserved;
diff --git a/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.cpp b/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.cpp
new file mode 100644
index 0000000..f85309e
--- /dev/null
+++ b/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.cpp
@@ -0,0 +1,91 @@
+/*
+ * Copyright (C) 2021 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Mac80211hwsim.h"
+
+#include "../../structs.h"
+#include "common.h"
+
+#include <libnl++/generic/families/mac80211_hwsim.h>
+
+namespace android::nl::protocols::generic::families {
+
+using DataType = AttributeDefinition::DataType;
+using Flags = AttributeDefinition::Flags;
+
+static void hwsim_tx_rateToStream(std::stringstream& ss, const Buffer<nlattr> attr);
+
+static const FlagsMap txControlFlags{
+        {HWSIM_TX_CTL_REQ_TX_STATUS, "REQ_TX"},
+        {HWSIM_TX_CTL_NO_ACK, "NO_ACK"},
+        {HWSIM_TX_STAT_ACK, "ACK"},
+};
+
+// clang-format off
+Mac80211hwsim::Mac80211hwsim(nlmsgtype_t familyId) : GenericMessageBase(familyId, "hwsim", {
+    {HWSIM_CMD_UNSPEC, "UNSPEC"},
+    {HWSIM_CMD_REGISTER, "REGISTER"},
+    {HWSIM_CMD_FRAME, "FRAME"},
+    {HWSIM_CMD_TX_INFO_FRAME, "TX_INFO_FRAME"},
+    {HWSIM_CMD_NEW_RADIO, "NEW_RADIO"},
+    {HWSIM_CMD_DEL_RADIO, "DEL_RADIO"},
+    {HWSIM_CMD_GET_RADIO, "GET_RADIO"},
+    {HWSIM_CMD_ADD_MAC_ADDR, "ADD_MAC_ADDR"},
+    {HWSIM_CMD_DEL_MAC_ADDR, "DEL_MAC_ADDR"},
+}, {
+    {HWSIM_ATTR_UNSPEC, {"UNSPEC"}},
+    {HWSIM_ATTR_ADDR_RECEIVER, {"ADDR_RECEIVER", DataType::Struct, hwaddrToStream}},
+    {HWSIM_ATTR_ADDR_TRANSMITTER, {"ADDR_TRANSMITTER", DataType::Struct, hwaddrToStream}},
+    {HWSIM_ATTR_FRAME, {"FRAME", DataType::Raw, AttributeMap{}, Flags::Verbose}},
+    {HWSIM_ATTR_FLAGS, {"FLAGS", DataType::Struct, flagsToStream(txControlFlags)}},
+    {HWSIM_ATTR_RX_RATE, {"RX_RATE", DataType::Uint}},
+    {HWSIM_ATTR_SIGNAL, {"SIGNAL", DataType::Uint}},
+    {HWSIM_ATTR_TX_INFO, {"TX_INFO", DataType::Struct, hwsim_tx_rateToStream}},
+    {HWSIM_ATTR_COOKIE, {"COOKIE", DataType::Uint}},
+    {HWSIM_ATTR_CHANNELS, {"CHANNELS", DataType::Uint}},
+    {HWSIM_ATTR_RADIO_ID, {"RADIO_ID", DataType::Uint}},
+    {HWSIM_ATTR_REG_HINT_ALPHA2, {"REG_HINT_ALPHA2", DataType::String}},
+    {HWSIM_ATTR_REG_CUSTOM_REG, {"REG_CUSTOM_REG", DataType::Uint}},
+    {HWSIM_ATTR_REG_STRICT_REG, {"REG_STRICT_REG", DataType::Flag}},
+    {HWSIM_ATTR_SUPPORT_P2P_DEVICE, {"SUPPORT_P2P_DEVICE", DataType::Flag}},
+    {HWSIM_ATTR_USE_CHANCTX, {"USE_CHANCTX", DataType::Flag}},
+    {HWSIM_ATTR_DESTROY_RADIO_ON_CLOSE, {"DESTROY_RADIO_ON_CLOSE", DataType::Flag}},
+    {HWSIM_ATTR_RADIO_NAME, {"RADIO_NAME", DataType::String}},
+    {HWSIM_ATTR_NO_VIF, {"NO_VIF", DataType::Flag}},
+    {HWSIM_ATTR_FREQ, {"FREQ", DataType::Uint}},
+    {HWSIM_ATTR_PAD, {"PAD", DataType::Uint}},
+    {HWSIM_ATTR_TX_INFO_FLAGS, {"TX_INFO_FLAGS"}},  // hwsim_tx_rate_flag
+    {HWSIM_ATTR_PERM_ADDR, {"PERM_ADDR"}},
+    {HWSIM_ATTR_IFTYPE_SUPPORT, {"IFTYPE_SUPPORT", DataType::Uint}},  // NL80211_IFTYPE_STATION etc
+    {HWSIM_ATTR_CIPHER_SUPPORT, {"CIPHER_SUPPORT", DataType::Struct, arrayToStream<int32_t>}},
+}) {}
+// clang-format on
+
+static void hwsim_tx_rateToStream(std::stringstream& ss, const Buffer<nlattr> attr) {
+    ss << '{';
+    bool first = true;
+    for (const auto rate : attr.data<hwsim_tx_rate>().getRaw()) {
+        if (rate.idx == -1) continue;
+
+        ss << (int)rate.idx << ": " << (unsigned)rate.count;
+
+        if (!first) ss << ", ";
+        first = false;
+    }
+    ss << '}';
+}
+
+}  // namespace android::nl::protocols::generic::families
diff --git a/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.h b/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.h
new file mode 100644
index 0000000..c01eb93
--- /dev/null
+++ b/automotive/can/1.0/default/libnl++/protocols/generic/families/Mac80211hwsim.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (C) 2021 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "../GenericMessageBase.h"
+
+namespace android::nl::protocols::generic::families {
+
+class Mac80211hwsim : public GenericMessageBase {
+  public:
+    Mac80211hwsim(nlmsgtype_t familyId);
+};
+
+}  // namespace android::nl::protocols::generic::families
diff --git a/automotive/can/1.0/default/libnl++/protocols/structs.cpp b/automotive/can/1.0/default/libnl++/protocols/structs.cpp
new file mode 100644
index 0000000..8ff71f0
--- /dev/null
+++ b/automotive/can/1.0/default/libnl++/protocols/structs.cpp
@@ -0,0 +1,57 @@
+/*
+ * Copyright (C) 2021 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "structs.h"
+
+#include <iomanip>
+
+namespace android::nl::protocols {
+
+AttributeDefinition::ToStream flagsToStream(FlagsMap flags) {
+    return [flags](std::stringstream& ss, const Buffer<nlattr> attr) {
+        auto val = attr.data<uint64_t>().copyFirst();
+
+        bool first = true;
+        for (const auto& [flag, name] : flags) {
+            if ((val & flag) != flag) continue;
+            val &= ~flag;
+
+            if (!first) ss << '|';
+            first = false;
+
+            ss << name;
+        }
+
+        if (val == 0) return;
+
+        if (!first) ss << '|';
+        ss << std::hex << val << std::dec;
+    };
+}
+
+void hwaddrToStream(std::stringstream& ss, const Buffer<nlattr> attr) {
+    ss << std::hex;
+    bool first = true;
+    for (const auto byte : attr.data<uint8_t>().getRaw()) {
+        if (!first) ss << ':';
+        first = false;
+
+        ss << std::setw(2) << unsigned(byte);
+    }
+    ss << std::dec;
+}
+
+}  // namespace android::nl::protocols
diff --git a/automotive/can/1.0/default/libnl++/protocols/structs.h b/automotive/can/1.0/default/libnl++/protocols/structs.h
index 44c17b8..f3a8c44 100644
--- a/automotive/can/1.0/default/libnl++/protocols/structs.h
+++ b/automotive/can/1.0/default/libnl++/protocols/structs.h
@@ -16,6 +16,8 @@
 
 #pragma once
 
+#include "MessageDefinition.h"
+
 #include <sstream>
 
 namespace android::nl::protocols {
@@ -30,4 +32,9 @@
     ss << '}';
 }
 
+typedef std::map<uint64_t, std::string> FlagsMap;
+AttributeDefinition::ToStream flagsToStream(FlagsMap flags);
+
+void hwaddrToStream(std::stringstream& ss, const Buffer<nlattr> attr);
+
 }  // namespace android::nl::protocols
diff --git a/neuralnetworks/1.0/utils/src/Conversions.cpp b/neuralnetworks/1.0/utils/src/Conversions.cpp
index 34862c0..700b050 100644
--- a/neuralnetworks/1.0/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.0/utils/src/Conversions.cpp
@@ -154,7 +154,7 @@
 }
 
 GeneralResult<SharedMemory> unvalidatedConvert(const hidl_memory& memory) {
-    return createSharedMemoryFromHidlMemory(memory);
+    return hal::utils::createSharedMemoryFromHidlMemory(memory);
 }
 
 GeneralResult<Model> unvalidatedConvert(const hal::V1_0::Model& model) {
@@ -162,7 +162,7 @@
 
     // Verify number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(model.operands.size(), operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(model.operands.size(), operations));
     CHECK(model.operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < model.operands.size(); ++i) {
         if (model.operands[i].numberOfConsumers != numberOfConsumers[i]) {
@@ -347,9 +347,7 @@
 }
 
 nn::GeneralResult<hidl_memory> unvalidatedConvert(const nn::SharedMemory& memory) {
-    CHECK(memory != nullptr);
-    return hidl_memory(memory->name, NN_TRY(hal::utils::hidlHandleFromSharedHandle(memory->handle)),
-                       memory->size);
+    return hal::utils::createHidlMemoryFromSharedMemory(memory);
 }
 
 nn::GeneralResult<Model> unvalidatedConvert(const nn::Model& model) {
@@ -362,7 +360,7 @@
 
     // Update number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(operands.size(), model.main.operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(operands.size(), model.main.operations));
     CHECK(operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < operands.size(); ++i) {
         operands[i].numberOfConsumers = numberOfConsumers[i];
diff --git a/neuralnetworks/1.1/utils/src/Conversions.cpp b/neuralnetworks/1.1/utils/src/Conversions.cpp
index 07bf7bc..d07f7d0 100644
--- a/neuralnetworks/1.1/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.1/utils/src/Conversions.cpp
@@ -111,7 +111,7 @@
 
     // Verify number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(model.operands.size(), operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(model.operands.size(), operations));
     CHECK(model.operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < model.operands.size(); ++i) {
         if (model.operands[i].numberOfConsumers != numberOfConsumers[i]) {
@@ -241,7 +241,7 @@
 
     // Update number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(operands.size(), model.main.operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(operands.size(), model.main.operations));
     CHECK(operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < operands.size(); ++i) {
         operands[i].numberOfConsumers = numberOfConsumers[i];
diff --git a/neuralnetworks/1.2/utils/src/Conversions.cpp b/neuralnetworks/1.2/utils/src/Conversions.cpp
index 5055650..86a417a 100644
--- a/neuralnetworks/1.2/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.2/utils/src/Conversions.cpp
@@ -227,7 +227,7 @@
 
     // Verify number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(model.operands.size(), operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(model.operands.size(), operations));
     CHECK(model.operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < model.operands.size(); ++i) {
         if (model.operands[i].numberOfConsumers != numberOfConsumers[i]) {
@@ -304,7 +304,11 @@
 }
 
 GeneralResult<SharedHandle> unvalidatedConvert(const hidl_handle& hidlHandle) {
-    return hal::utils::sharedHandleFromNativeHandle(hidlHandle.getNativeHandle());
+    if (hidlHandle.getNativeHandle() == nullptr) {
+        return nullptr;
+    }
+    auto handle = NN_TRY(hal::utils::sharedHandleFromNativeHandle(hidlHandle.getNativeHandle()));
+    return std::make_shared<const Handle>(std::move(handle));
 }
 
 GeneralResult<DeviceType> convert(const hal::V1_2::DeviceType& deviceType) {
@@ -525,7 +529,7 @@
 
     // Update number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(operands.size(), model.main.operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(operands.size(), model.main.operations));
     CHECK(operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < operands.size(); ++i) {
         operands[i].numberOfConsumers = numberOfConsumers[i];
@@ -588,7 +592,10 @@
 }
 
 nn::GeneralResult<hidl_handle> unvalidatedConvert(const nn::SharedHandle& handle) {
-    return hal::utils::hidlHandleFromSharedHandle(handle);
+    if (handle == nullptr) {
+        return {};
+    }
+    return hal::utils::hidlHandleFromSharedHandle(*handle);
 }
 
 nn::GeneralResult<DeviceType> convert(const nn::DeviceType& deviceType) {
diff --git a/neuralnetworks/1.3/utils/src/Conversions.cpp b/neuralnetworks/1.3/utils/src/Conversions.cpp
index d96588e..320c74c 100644
--- a/neuralnetworks/1.3/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.3/utils/src/Conversions.cpp
@@ -217,7 +217,7 @@
 
     // Verify number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(subgraph.operands.size(), operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(subgraph.operands.size(), operations));
     CHECK(subgraph.operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < subgraph.operands.size(); ++i) {
         if (subgraph.operands[i].numberOfConsumers != numberOfConsumers[i]) {
@@ -261,7 +261,7 @@
     using Discriminator = hal::V1_3::Request::MemoryPool::hidl_discriminator;
     switch (memoryPool.getDiscriminator()) {
         case Discriminator::hidlMemory:
-            return createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
+            return hal::utils::createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
         case Discriminator::token:
             return static_cast<Request::MemoryDomainToken>(memoryPool.token());
     }
@@ -559,7 +559,7 @@
 
     // Update number of consumers.
     const auto numberOfConsumers =
-            hal::utils::countNumberOfConsumers(operands.size(), subgraph.operations);
+            NN_TRY(hal::utils::countNumberOfConsumers(operands.size(), subgraph.operations));
     CHECK(operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < operands.size(); ++i) {
         operands[i].numberOfConsumers = numberOfConsumers[i];
diff --git a/neuralnetworks/TEST_MAPPING b/neuralnetworks/TEST_MAPPING
index de84624..5d168d2 100644
--- a/neuralnetworks/TEST_MAPPING
+++ b/neuralnetworks/TEST_MAPPING
@@ -60,6 +60,17 @@
           "include-filter": "-*sample_float_fast*:*sample_float_slow*:*sample_minimal*:*sample_quant*"
         }
       ]
+    },
+    {
+      "name": "VtsHalNeuralnetworksTargetTest",
+      "options": [
+        {
+          // Do not use any sample driver except sample-all in order to reduce
+          // testing time. The other sample drivers (fast-float, quant, etc.)
+          // are subsets of sample-all.
+          "include-filter": "-*sample_float_fast*:*sample_float_slow*:*sample_minimal*:*sample_quant*"
+        }
+      ]
     }
   ]
 }
diff --git a/neuralnetworks/aidl/utils/Android.bp b/neuralnetworks/aidl/utils/Android.bp
index 56017da..147d401 100644
--- a/neuralnetworks/aidl/utils/Android.bp
+++ b/neuralnetworks/aidl/utils/Android.bp
@@ -21,12 +21,14 @@
     local_include_dirs: ["include/nnapi/hal/aidl/"],
     export_include_dirs: ["include"],
     static_libs: [
+        "libarect",
         "neuralnetworks_types",
         "neuralnetworks_utils_hal_common",
     ],
     shared_libs: [
-        "libhidlbase",
         "android.hardware.neuralnetworks-V1-ndk_platform",
         "libbinder_ndk",
+        "libhidlbase",
+        "libnativewindow",
     ],
 }
diff --git a/neuralnetworks/aidl/utils/src/Conversions.cpp b/neuralnetworks/aidl/utils/src/Conversions.cpp
index 486d01b..db3504b 100644
--- a/neuralnetworks/aidl/utils/src/Conversions.cpp
+++ b/neuralnetworks/aidl/utils/src/Conversions.cpp
@@ -18,6 +18,8 @@
 
 #include <aidl/android/hardware/common/NativeHandle.h>
 #include <android-base/logging.h>
+#include <android/hardware_buffer.h>
+#include <cutils/native_handle.h>
 #include <nnapi/OperandTypes.h>
 #include <nnapi/OperationTypes.h>
 #include <nnapi/Result.h>
@@ -27,6 +29,7 @@
 #include <nnapi/Validation.h>
 #include <nnapi/hal/CommonUtils.h>
 #include <nnapi/hal/HandleError.h>
+#include <vndk/hardware_buffer.h>
 
 #include <algorithm>
 #include <chrono>
@@ -127,6 +130,61 @@
     return canonical;
 }
 
+GeneralResult<Handle> unvalidatedConvertHelper(const NativeHandle& aidlNativeHandle) {
+    std::vector<base::unique_fd> fds;
+    fds.reserve(aidlNativeHandle.fds.size());
+    for (const auto& fd : aidlNativeHandle.fds) {
+        const int dupFd = dup(fd.get());
+        if (dupFd == -1) {
+            // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
+            // here?
+            return NN_ERROR() << "Failed to dup the fd";
+        }
+        fds.emplace_back(dupFd);
+    }
+
+    return Handle{.fds = std::move(fds), .ints = aidlNativeHandle.ints};
+}
+
+struct NativeHandleDeleter {
+    void operator()(native_handle_t* handle) const {
+        if (handle) {
+            native_handle_close(handle);
+            native_handle_delete(handle);
+        }
+    }
+};
+
+using UniqueNativeHandle = std::unique_ptr<native_handle_t, NativeHandleDeleter>;
+
+static nn::GeneralResult<UniqueNativeHandle> nativeHandleFromAidlHandle(
+        const NativeHandle& handle) {
+    std::vector<base::unique_fd> fds;
+    fds.reserve(handle.fds.size());
+    for (const auto& fd : handle.fds) {
+        const int dupFd = dup(fd.get());
+        if (dupFd == -1) {
+            return NN_ERROR() << "Failed to dup the fd";
+        }
+        fds.emplace_back(dupFd);
+    }
+
+    constexpr size_t kIntMax = std::numeric_limits<int>::max();
+    CHECK_LE(handle.fds.size(), kIntMax);
+    CHECK_LE(handle.ints.size(), kIntMax);
+    native_handle_t* nativeHandle = native_handle_create(static_cast<int>(handle.fds.size()),
+                                                         static_cast<int>(handle.ints.size()));
+    if (nativeHandle == nullptr) {
+        return NN_ERROR() << "Failed to create native_handle";
+    }
+    for (size_t i = 0; i < fds.size(); ++i) {
+        nativeHandle->data[i] = fds[i].release();
+    }
+    std::copy(handle.ints.begin(), handle.ints.end(), &nativeHandle->data[nativeHandle->numFds]);
+
+    return UniqueNativeHandle(nativeHandle);
+}
+
 }  // anonymous namespace
 
 GeneralResult<OperandType> unvalidatedConvert(const aidl_hal::OperandType& operandType) {
@@ -318,10 +376,64 @@
     return measureTiming ? MeasureTiming::YES : MeasureTiming::NO;
 }
 
+static uint32_t roundUpToMultiple(uint32_t value, uint32_t multiple) {
+    return (value + multiple - 1) / multiple * multiple;
+}
+
 GeneralResult<SharedMemory> unvalidatedConvert(const aidl_hal::Memory& memory) {
     VERIFY_NON_NEGATIVE(memory.size) << "Memory size must not be negative";
+    if (memory.size > std::numeric_limits<uint32_t>::max()) {
+        return NN_ERROR() << "Memory: size must be <= std::numeric_limits<size_t>::max()";
+    }
+
+    if (memory.name != "hardware_buffer_blob") {
+        return std::make_shared<const Memory>(Memory{
+                .handle = NN_TRY(unvalidatedConvertHelper(memory.handle)),
+                .size = static_cast<uint32_t>(memory.size),
+                .name = memory.name,
+        });
+    }
+
+    const auto size = static_cast<uint32_t>(memory.size);
+    const auto format = AHARDWAREBUFFER_FORMAT_BLOB;
+    const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
+    const uint32_t width = size;
+    const uint32_t height = 1;  // height is always 1 for BLOB mode AHardwareBuffer.
+    const uint32_t layers = 1;  // layers is always 1 for BLOB mode AHardwareBuffer.
+
+    const UniqueNativeHandle handle = NN_TRY(nativeHandleFromAidlHandle(memory.handle));
+    const native_handle_t* nativeHandle = handle.get();
+
+    // AHardwareBuffer_createFromHandle() might fail because an allocator
+    // expects a specific stride value. In that case, we try to guess it by
+    // aligning the width to small powers of 2.
+    // TODO(b/174120849): Avoid stride assumptions.
+    AHardwareBuffer* hardwareBuffer = nullptr;
+    status_t status = UNKNOWN_ERROR;
+    for (uint32_t alignment : {1, 4, 32, 64, 128, 2, 8, 16}) {
+        const uint32_t stride = roundUpToMultiple(width, alignment);
+        AHardwareBuffer_Desc desc{
+                .width = width,
+                .height = height,
+                .layers = layers,
+                .format = format,
+                .usage = usage,
+                .stride = stride,
+        };
+        status = AHardwareBuffer_createFromHandle(&desc, nativeHandle,
+                                                  AHARDWAREBUFFER_CREATE_FROM_HANDLE_METHOD_CLONE,
+                                                  &hardwareBuffer);
+        if (status == NO_ERROR) {
+            break;
+        }
+    }
+    if (status != NO_ERROR) {
+        return NN_ERROR(ErrorStatus::GENERAL_FAILURE)
+               << "Can't create AHardwareBuffer from handle. Error: " << status;
+    }
+
     return std::make_shared<const Memory>(Memory{
-            .handle = NN_TRY(unvalidatedConvert(memory.handle)),
+            .handle = HardwareBufferHandle(hardwareBuffer, /*takeOwnership=*/true),
             .size = static_cast<uint32_t>(memory.size),
             .name = memory.name,
     });
@@ -400,22 +512,7 @@
 }
 
 GeneralResult<SharedHandle> unvalidatedConvert(const NativeHandle& aidlNativeHandle) {
-    std::vector<base::unique_fd> fds;
-    fds.reserve(aidlNativeHandle.fds.size());
-    for (const auto& fd : aidlNativeHandle.fds) {
-        int dupFd = dup(fd.get());
-        if (dupFd == -1) {
-            // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
-            // here?
-            return NN_ERROR() << "Failed to dup the fd";
-        }
-        fds.emplace_back(dupFd);
-    }
-
-    return std::make_shared<const Handle>(Handle{
-            .fds = std::move(fds),
-            .ints = aidlNativeHandle.ints,
-    });
+    return std::make_shared<const Handle>(NN_TRY(unvalidatedConvertHelper(aidlNativeHandle)));
 }
 
 GeneralResult<ExecutionPreference> convert(
@@ -508,13 +605,11 @@
     return halObject;
 }
 
-}  // namespace
-
-nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandle& sharedHandle) {
+nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::Handle& handle) {
     common::NativeHandle aidlNativeHandle;
-    aidlNativeHandle.fds.reserve(sharedHandle->fds.size());
-    for (const auto& fd : sharedHandle->fds) {
-        int dupFd = dup(fd.get());
+    aidlNativeHandle.fds.reserve(handle.fds.size());
+    for (const auto& fd : handle.fds) {
+        const int dupFd = dup(fd.get());
         if (dupFd == -1) {
             // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
             // here?
@@ -522,17 +617,69 @@
         }
         aidlNativeHandle.fds.emplace_back(dupFd);
     }
-    aidlNativeHandle.ints = sharedHandle->ints;
+    aidlNativeHandle.ints = handle.ints;
     return aidlNativeHandle;
 }
 
+static nn::GeneralResult<common::NativeHandle> aidlHandleFromNativeHandle(
+        const native_handle_t& handle) {
+    common::NativeHandle aidlNativeHandle;
+
+    aidlNativeHandle.fds.reserve(handle.numFds);
+    for (int i = 0; i < handle.numFds; ++i) {
+        const int dupFd = dup(handle.data[i]);
+        if (dupFd == -1) {
+            return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
+        }
+        aidlNativeHandle.fds.emplace_back(dupFd);
+    }
+
+    aidlNativeHandle.ints = std::vector<int>(&handle.data[handle.numFds],
+                                             &handle.data[handle.numFds + handle.numInts]);
+
+    return aidlNativeHandle;
+}
+
+}  // namespace
+
+nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandle& sharedHandle) {
+    CHECK(sharedHandle != nullptr);
+    return unvalidatedConvert(*sharedHandle);
+}
+
 nn::GeneralResult<Memory> unvalidatedConvert(const nn::SharedMemory& memory) {
     CHECK(memory != nullptr);
     if (memory->size > std::numeric_limits<int64_t>::max()) {
         return NN_ERROR() << "Memory size doesn't fit into int64_t.";
     }
+    if (const auto* handle = std::get_if<nn::Handle>(&memory->handle)) {
+        return Memory{
+                .handle = NN_TRY(unvalidatedConvert(*handle)),
+                .size = static_cast<int64_t>(memory->size),
+                .name = memory->name,
+        };
+    }
+
+    const auto* ahwb = std::get<nn::HardwareBufferHandle>(memory->handle).get();
+    AHardwareBuffer_Desc bufferDesc;
+    AHardwareBuffer_describe(ahwb, &bufferDesc);
+
+    if (bufferDesc.format == AHARDWAREBUFFER_FORMAT_BLOB) {
+        CHECK_EQ(memory->size, bufferDesc.width);
+        CHECK_EQ(memory->name, "hardware_buffer_blob");
+    } else {
+        CHECK_EQ(memory->size, 0u);
+        CHECK_EQ(memory->name, "hardware_buffer");
+    }
+
+    const native_handle_t* nativeHandle = AHardwareBuffer_getNativeHandle(ahwb);
+    if (nativeHandle == nullptr) {
+        return NN_ERROR() << "unvalidatedConvert failed because AHardwareBuffer_getNativeHandle "
+                             "returned nullptr";
+    }
+
     return Memory{
-            .handle = NN_TRY(unvalidatedConvert(memory->handle)),
+            .handle = NN_TRY(aidlHandleFromNativeHandle(*nativeHandle)),
             .size = static_cast<int64_t>(memory->size),
             .name = memory->name,
     };
diff --git a/neuralnetworks/aidl/vts/functional/Utils.cpp b/neuralnetworks/aidl/vts/functional/Utils.cpp
index 14a496a..3c7f5f7 100644
--- a/neuralnetworks/aidl/vts/functional/Utils.cpp
+++ b/neuralnetworks/aidl/vts/functional/Utils.cpp
@@ -135,7 +135,8 @@
     ASSERT_EQ(AHardwareBuffer_allocate(&desc, &mAhwb), 0);
     ASSERT_NE(mAhwb, nullptr);
 
-    const auto sharedMemory = nn::createSharedMemoryFromAHWB(*mAhwb).value();
+    const auto sharedMemory =
+            nn::createSharedMemoryFromAHWB(mAhwb, /*takeOwnership=*/false).value();
     mMapping = nn::map(sharedMemory).value();
     mPtr = static_cast<uint8_t*>(std::get<void*>(mMapping.pointer));
     CHECK_NE(mPtr, nullptr);
diff --git a/neuralnetworks/aidl/vts/functional/ValidateModel.cpp b/neuralnetworks/aidl/vts/functional/ValidateModel.cpp
index b84d981..6d84e1e 100644
--- a/neuralnetworks/aidl/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/aidl/vts/functional/ValidateModel.cpp
@@ -1310,8 +1310,10 @@
 ////////////////////////// ENTRY POINT //////////////////////////////
 
 void validateModel(const std::shared_ptr<IDevice>& device, const Model& model) {
-    const auto numberOfConsumers = nn::countNumberOfConsumers(
-            model.main.operands.size(), nn::convert(model.main.operations).value());
+    const auto numberOfConsumers =
+            nn::countNumberOfConsumers(model.main.operands.size(),
+                                       nn::convert(model.main.operations).value())
+                    .value();
     mutateExecutionOrderTest(device, model, numberOfConsumers);
     mutateOperandTypeTest(device, model);
     mutateOperandRankTest(device, model);
diff --git a/neuralnetworks/utils/adapter/Android.bp b/neuralnetworks/utils/adapter/Android.bp
new file mode 100644
index 0000000..e8dc3e7
--- /dev/null
+++ b/neuralnetworks/utils/adapter/Android.bp
@@ -0,0 +1,37 @@
+//
+// Copyright (C) 2020 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+cc_library_static {
+    name: "neuralnetworks_utils_hal_adapter",
+    defaults: ["neuralnetworks_utils_defaults"],
+    srcs: ["src/*"],
+    local_include_dirs: ["include/nnapi/hal"],
+    export_include_dirs: ["include"],
+    static_libs: [
+        "neuralnetworks_types",
+        "neuralnetworks_utils_hal_1_0",
+        "neuralnetworks_utils_hal_1_1",
+        "neuralnetworks_utils_hal_1_2",
+        "neuralnetworks_utils_hal_1_3",
+    ],
+    shared_libs: [
+        "android.hardware.neuralnetworks@1.0",
+        "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.2",
+        "android.hardware.neuralnetworks@1.3",
+        "libfmq",
+    ],
+}
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h
new file mode 100644
index 0000000..da00a09
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h
@@ -0,0 +1,72 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_ADAPTER_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_ADAPTER_H
+
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <sys/types.h>
+#include <functional>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+/**
+ * A self-contained unit of work to be executed.
+ */
+using Task = std::function<void()>;
+
+/**
+ * A type-erased executor which executes a task asynchronously.
+ *
+ * This executor is also provided with an Application ID (Android User ID) and an optional deadline
+ * for when the caller expects is the upper bound for the amount of time to complete the task.
+ */
+using Executor = std::function<void(Task, uid_t, nn::OptionalTimePoint)>;
+
+/**
+ * Adapt an NNAPI canonical interface object to a HIDL NN HAL interface object.
+ *
+ * The IPreparedModel object created from IDevice::prepareModel or IDevice::preparedModelFromCache
+ * must return "const nn::Model*" from IPreparedModel::getUnderlyingResource().
+ *
+ * @param device NNAPI canonical IDevice interface object to be adapted.
+ * @param executor Type-erased executor to handle executing tasks asynchronously.
+ * @return HIDL NN HAL IDevice interface object.
+ */
+sp<V1_3::IDevice> adapt(nn::SharedDevice device, Executor executor);
+
+/**
+ * Adapt an NNAPI canonical interface object to a HIDL NN HAL interface object.
+ *
+ * The IPreparedModel object created from IDevice::prepareModel or IDevice::preparedModelFromCache
+ * must return "const nn::Model*" from IPreparedModel::getUnderlyingResource().
+ *
+ * This function uses a default executor, which will execute tasks from a detached thread.
+ *
+ * @param device NNAPI canonical IDevice interface object to be adapted.
+ * @return HIDL NN HAL IDevice interface object.
+ */
+sp<V1_3::IDevice> adapt(nn::SharedDevice device);
+
+}  // namespace android::hardware::neuralnetworks::adapter
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_ADAPTER_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h
new file mode 100644
index 0000000..e53c7d4
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h
@@ -0,0 +1,46 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_BUFFER_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_BUFFER_H
+
+#include <android/hardware/neuralnetworks/1.3/IBuffer.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+// Class that adapts nn::IBuffer to V1_3::IBuffer.
+class Buffer final : public V1_3::IBuffer {
+  public:
+    explicit Buffer(nn::SharedBuffer buffer);
+
+    Return<V1_3::ErrorStatus> copyTo(const hidl_memory& dst) override;
+    Return<V1_3::ErrorStatus> copyFrom(const hidl_memory& src,
+                                       const hidl_vec<uint32_t>& dimensions) override;
+
+  private:
+    const nn::SharedBuffer kBuffer;
+};
+
+}  // namespace android::hardware::neuralnetworks::adapter
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_BUFFER_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h
new file mode 100644
index 0000000..148d0a0
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h
@@ -0,0 +1,96 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_DEVICE_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_DEVICE_H
+
+#include "nnapi/hal/Adapter.h"
+
+#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+using CacheToken = hidl_array<uint8_t, nn::kByteSizeOfCacheToken>;
+
+// Class that adapts nn::IDevice to V1_3::IDevice.
+class Device final : public V1_3::IDevice {
+  public:
+    Device(nn::SharedDevice device, Executor executor);
+
+    Return<void> getCapabilities(getCapabilities_cb cb) override;
+    Return<void> getCapabilities_1_1(getCapabilities_1_1_cb cb) override;
+    Return<void> getCapabilities_1_2(getCapabilities_1_2_cb cb) override;
+    Return<void> getCapabilities_1_3(getCapabilities_1_3_cb cb) override;
+    Return<void> getVersionString(getVersionString_cb cb) override;
+    Return<void> getType(getType_cb cb) override;
+    Return<void> getSupportedExtensions(getSupportedExtensions_cb) override;
+    Return<void> getSupportedOperations(const V1_0::Model& model,
+                                        getSupportedOperations_cb cb) override;
+    Return<void> getSupportedOperations_1_1(const V1_1::Model& model,
+                                            getSupportedOperations_1_1_cb cb) override;
+    Return<void> getSupportedOperations_1_2(const V1_2::Model& model,
+                                            getSupportedOperations_1_2_cb cb) override;
+    Return<void> getSupportedOperations_1_3(const V1_3::Model& model,
+                                            getSupportedOperations_1_3_cb cb) override;
+    Return<void> getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb) override;
+    Return<V1_0::ErrorStatus> prepareModel(
+            const V1_0::Model& model, const sp<V1_0::IPreparedModelCallback>& callback) override;
+    Return<V1_0::ErrorStatus> prepareModel_1_1(
+            const V1_1::Model& model, V1_1::ExecutionPreference preference,
+            const sp<V1_0::IPreparedModelCallback>& callback) override;
+    Return<V1_0::ErrorStatus> prepareModel_1_2(
+            const V1_2::Model& model, V1_1::ExecutionPreference preference,
+            const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+            const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) override;
+    Return<V1_3::ErrorStatus> prepareModel_1_3(
+            const V1_3::Model& model, V1_1::ExecutionPreference preference, V1_3::Priority priority,
+            const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+            const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+            const sp<V1_3::IPreparedModelCallback>& callback) override;
+    Return<V1_0::ErrorStatus> prepareModelFromCache(
+            const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+            const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) override;
+    Return<V1_3::ErrorStatus> prepareModelFromCache_1_3(
+            const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+            const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+            const sp<V1_3::IPreparedModelCallback>& callback) override;
+    Return<V1_0::DeviceStatus> getStatus() override;
+    Return<void> allocate(const V1_3::BufferDesc& desc,
+                          const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+                          const hidl_vec<V1_3::BufferRole>& inputRoles,
+                          const hidl_vec<V1_3::BufferRole>& outputRoles, allocate_cb cb) override;
+
+  private:
+    const nn::SharedDevice kDevice;
+    const Executor kExecutor;
+};
+
+}  // namespace android::hardware::neuralnetworks::adapter
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_DEVICE_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.h b/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.h
new file mode 100644
index 0000000..65763b8
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.h
@@ -0,0 +1,79 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_PREPARED_MODEL_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_PREPARED_MODEL_H
+
+#include "nnapi/hal/Adapter.h"
+
+#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+// Class that adapts nn::IPreparedModel to V1_3::IPreparedModel.
+class PreparedModel final : public V1_3::IPreparedModel {
+  public:
+    PreparedModel(nn::SharedPreparedModel preparedModel, Executor executor, uid_t userId);
+
+    Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
+                                      const sp<V1_0::IExecutionCallback>& callback) override;
+    Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, V1_2::MeasureTiming measure,
+                                          const sp<V1_2::IExecutionCallback>& callback) override;
+    Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request, V1_2::MeasureTiming measure,
+                                          const V1_3::OptionalTimePoint& deadline,
+                                          const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+                                          const sp<V1_3::IExecutionCallback>& callback) override;
+    Return<void> executeSynchronously(const V1_0::Request& request, V1_2::MeasureTiming measure,
+                                      executeSynchronously_cb cb) override;
+    Return<void> executeSynchronously_1_3(const V1_3::Request& request, V1_2::MeasureTiming measure,
+                                          const V1_3::OptionalTimePoint& deadline,
+                                          const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+                                          executeSynchronously_1_3_cb cb) override;
+    Return<void> configureExecutionBurst(
+            const sp<V1_2::IBurstCallback>& callback,
+            const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+            const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+            configureExecutionBurst_cb cb) override;
+    Return<void> executeFenced(const V1_3::Request& request, const hidl_vec<hidl_handle>& waitFor,
+                               V1_2::MeasureTiming measure, const V1_3::OptionalTimePoint& deadline,
+                               const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+                               const V1_3::OptionalTimeoutDuration& duration,
+                               executeFenced_cb callback) override;
+
+    nn::SharedPreparedModel getUnderlyingPreparedModel() const;
+
+  private:
+    const nn::SharedPreparedModel kPreparedModel;
+    const Executor kExecutor;
+    const uid_t kUserId;
+};
+
+}  // namespace android::hardware::neuralnetworks::adapter
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_PREPARED_MODEL_H
diff --git a/neuralnetworks/utils/adapter/src/Adapter.cpp b/neuralnetworks/utils/adapter/src/Adapter.cpp
new file mode 100644
index 0000000..d6f53f0
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Adapter.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Adapter.h"
+
+#include "Device.h"
+
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <sys/types.h>
+
+#include <functional>
+#include <memory>
+#include <thread>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+sp<V1_3::IDevice> adapt(nn::SharedDevice device, Executor executor) {
+    return sp<Device>::make(std::move(device), std::move(executor));
+}
+
+sp<V1_3::IDevice> adapt(nn::SharedDevice device) {
+    Executor defaultExecutor = [](Task task, uid_t /*uid*/, nn::OptionalTimePoint /*deadline*/) {
+        std::thread(std::move(task)).detach();
+    };
+    return adapt(std::move(device), std::move(defaultExecutor));
+}
+
+}  // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/Buffer.cpp b/neuralnetworks/utils/adapter/src/Buffer.cpp
new file mode 100644
index 0000000..3a04bf6
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Buffer.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Buffer.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.3/IBuffer.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+    auto result = nn::convert(object);
+    if (!result.has_value()) {
+        result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+    }
+    return result;
+}
+
+nn::GeneralResult<void> copyTo(const nn::SharedBuffer& buffer, const hidl_memory& dst) {
+    const auto memory = NN_TRY(convertInput(dst));
+    NN_TRY(buffer->copyTo(memory));
+    return {};
+}
+
+nn::GeneralResult<void> copyFrom(const nn::SharedBuffer& buffer, const hidl_memory& src,
+                                 const hidl_vec<uint32_t>& dimensions) {
+    const auto memory = NN_TRY(convertInput(src));
+    NN_TRY(buffer->copyFrom(memory, dimensions));
+    return {};
+}
+
+}  // namespace
+
+Buffer::Buffer(nn::SharedBuffer buffer) : kBuffer(std::move(buffer)) {
+    CHECK(kBuffer != nullptr);
+}
+
+Return<V1_3::ErrorStatus> Buffer::copyTo(const hidl_memory& dst) {
+    auto result = adapter::copyTo(kBuffer, dst);
+    if (!result.has_value()) {
+        const auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Buffer::copyTo failed with " << code << ": " << message;
+        return V1_3::utils::convert(code).value();
+    }
+    return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Buffer::copyFrom(const hidl_memory& src,
+                                           const hidl_vec<uint32_t>& dimensions) {
+    auto result = adapter::copyFrom(kBuffer, src, dimensions);
+    if (!result.has_value()) {
+        const auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Buffer::copyFrom failed with " << code << ": " << message;
+        return V1_3::utils::convert(code).value();
+    }
+    return V1_3::ErrorStatus::NONE;
+}
+
+}  // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/Device.cpp b/neuralnetworks/utils/adapter/src/Device.cpp
new file mode 100644
index 0000000..96142c3
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Device.cpp
@@ -0,0 +1,556 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Device.h"
+
+#include "Buffer.h"
+#include "PreparedModel.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <hwbinder/IPCThreadState.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/Result.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.0/Utils.h>
+#include <nnapi/hal/1.1/Conversions.h>
+#include <nnapi/hal/1.1/Utils.h>
+#include <nnapi/hal/1.2/Conversions.h>
+#include <nnapi/hal/1.2/Utils.h>
+#include <nnapi/hal/1.3/Conversions.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <sys/types.h>
+
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+    auto result = nn::convert(object);
+    if (!result.has_value()) {
+        result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+    }
+    return result;
+}
+
+using PrepareModelResult = nn::GeneralResult<nn::SharedPreparedModel>;
+
+sp<PreparedModel> adaptPreparedModel(nn::SharedPreparedModel preparedModel, Executor executor,
+                                     uid_t userId) {
+    if (preparedModel == nullptr) {
+        return nullptr;
+    }
+    return sp<PreparedModel>::make(std::move(preparedModel), std::move(executor), userId);
+}
+
+void notify(V1_0::IPreparedModelCallback* callback, nn::ErrorStatus status,
+            const sp<PreparedModel>& hidlPreparedModel) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_0::utils::convert(status).value();
+        const auto ret = callback->notify(hidlStatus, hidlPreparedModel);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_0::IPreparedModelCallback::notify failed with " << ret.description();
+        }
+    }
+}
+
+void notify(V1_2::IPreparedModelCallback* callback, nn::ErrorStatus status,
+            const sp<PreparedModel>& hidlPreparedModel) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_2::utils::convert(status).value();
+        const auto ret = callback->notify_1_2(hidlStatus, hidlPreparedModel);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_2::IPreparedModelCallback::notify_1_2 failed with "
+                       << ret.description();
+        }
+    }
+}
+
+void notify(V1_3::IPreparedModelCallback* callback, nn::ErrorStatus status,
+            const sp<PreparedModel>& hidlPreparedModel) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_3::utils::convert(status).value();
+        const auto ret = callback->notify_1_3(hidlStatus, hidlPreparedModel);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_3::IPreparedModelCallback::notify_1_3 failed with "
+                       << ret.description();
+        }
+    }
+}
+
+template <typename CallbackType>
+void notify(CallbackType* callback, PrepareModelResult result, Executor executor, uid_t userId) {
+    if (!result.has_value()) {
+        const auto [message, status] = std::move(result).error();
+        LOG(ERROR) << message;
+        notify(callback, status, nullptr);
+    } else {
+        auto preparedModel = std::move(result).value();
+        auto hidlPreparedModel =
+                adaptPreparedModel(std::move(preparedModel), std::move(executor), userId);
+        notify(callback, nn::ErrorStatus::NONE, std::move(hidlPreparedModel));
+    }
+}
+
+template <typename ModelType>
+nn::GeneralResult<hidl_vec<bool>> getSupportedOperations(const nn::SharedDevice& device,
+                                                         const ModelType& model) {
+    const auto nnModel = NN_TRY(convertInput(model));
+    return NN_TRY(device->getSupportedOperations(nnModel));
+}
+
+nn::GeneralResult<void> prepareModel(const nn::SharedDevice& device, const Executor& executor,
+                                     const V1_0::Model& model,
+                                     const sp<V1_0::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnModel = NN_TRY(convertInput(model));
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    Task task = [device, nnModel = std::move(nnModel), userId, executor, callback] {
+        auto result = device->prepareModel(nnModel, nn::ExecutionPreference::DEFAULT,
+                                           nn::Priority::DEFAULT, {}, {}, {}, {});
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_1(const nn::SharedDevice& device, const Executor& executor,
+                                         const V1_1::Model& model,
+                                         V1_1::ExecutionPreference preference,
+                                         const sp<V1_0::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnModel = NN_TRY(convertInput(model));
+    const auto nnPreference = NN_TRY(convertInput(preference));
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    Task task = [device, nnModel = std::move(nnModel), nnPreference, userId, executor, callback] {
+        auto result =
+                device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {}, {}, {}, {});
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_2(const nn::SharedDevice& device, const Executor& executor,
+                                         const V1_2::Model& model,
+                                         V1_1::ExecutionPreference preference,
+                                         const hidl_vec<hidl_handle>& modelCache,
+                                         const hidl_vec<hidl_handle>& dataCache,
+                                         const CacheToken& token,
+                                         const sp<V1_2::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnModel = NN_TRY(convertInput(model));
+    const auto nnPreference = NN_TRY(convertInput(preference));
+    auto nnModelCache = NN_TRY(convertInput(modelCache));
+    auto nnDataCache = NN_TRY(convertInput(dataCache));
+    const auto nnToken = nn::CacheToken(token);
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    Task task = [device, nnModel = std::move(nnModel), nnPreference,
+                 nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache),
+                 nnToken, userId, executor, callback] {
+        auto result = device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {},
+                                           nnModelCache, nnDataCache, nnToken);
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_3(
+        const nn::SharedDevice& device, const Executor& executor, const V1_3::Model& model,
+        V1_1::ExecutionPreference preference, V1_3::Priority priority,
+        const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+        const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+        const sp<V1_3::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnModel = NN_TRY(convertInput(model));
+    const auto nnPreference = NN_TRY(convertInput(preference));
+    const auto nnPriority = NN_TRY(convertInput(priority));
+    const auto nnDeadline = NN_TRY(convertInput(deadline));
+    auto nnModelCache = NN_TRY(convertInput(modelCache));
+    auto nnDataCache = NN_TRY(convertInput(dataCache));
+    const auto nnToken = nn::CacheToken(token);
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    Task task = [device, nnModel = std::move(nnModel), nnPreference, nnPriority, nnDeadline,
+                 nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache),
+                 nnToken, userId, executor, callback] {
+        auto result = device->prepareModel(nnModel, nnPreference, nnPriority, nnDeadline,
+                                           nnModelCache, nnDataCache, nnToken);
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, nnDeadline);
+
+    return {};
+}
+
+nn::GeneralResult<void> prepareModelFromCache(const nn::SharedDevice& device,
+                                              const Executor& executor,
+                                              const hidl_vec<hidl_handle>& modelCache,
+                                              const hidl_vec<hidl_handle>& dataCache,
+                                              const CacheToken& token,
+                                              const sp<V1_2::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnModelCache = NN_TRY(convertInput(modelCache));
+    auto nnDataCache = NN_TRY(convertInput(dataCache));
+    const auto nnToken = nn::CacheToken(token);
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    Task task = [device, nnModelCache = std::move(nnModelCache),
+                 nnDataCache = std::move(nnDataCache), nnToken, userId, executor, callback] {
+        auto result = device->prepareModelFromCache({}, nnModelCache, nnDataCache, nnToken);
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> prepareModelFromCache_1_3(
+        const nn::SharedDevice& device, const Executor& executor,
+        const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+        const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+        const sp<V1_3::IPreparedModelCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    const auto nnDeadline = NN_TRY(convertInput(deadline));
+    auto nnModelCache = NN_TRY(convertInput(modelCache));
+    auto nnDataCache = NN_TRY(convertInput(dataCache));
+    const auto nnToken = nn::CacheToken(token);
+
+    const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+    auto task = [device, nnDeadline, nnModelCache = std::move(nnModelCache),
+                 nnDataCache = std::move(nnDataCache), nnToken, userId, executor, callback] {
+        auto result = device->prepareModelFromCache(nnDeadline, nnModelCache, nnDataCache, nnToken);
+        notify(callback.get(), std::move(result), executor, userId);
+    };
+    executor(std::move(task), userId, nnDeadline);
+
+    return {};
+}
+
+nn::GeneralResult<nn::SharedPreparedModel> downcast(const sp<V1_3::IPreparedModel>& preparedModel) {
+    if (preparedModel == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "preparedModel is nullptr";
+    }
+    if (preparedModel->isRemote()) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Cannot convert remote models";
+    }
+
+    // This static_cast is safe because adapter::PreparedModel is the only class that implements
+    // the IPreparedModel interface in the adapter service code.
+    const auto* casted = static_cast<const PreparedModel*>(preparedModel.get());
+    return casted->getUnderlyingPreparedModel();
+}
+
+nn::GeneralResult<std::vector<nn::SharedPreparedModel>> downcastAll(
+        const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels) {
+    std::vector<nn::SharedPreparedModel> canonical;
+    canonical.reserve(preparedModels.size());
+    for (const auto& preparedModel : preparedModels) {
+        canonical.push_back(NN_TRY(downcast(preparedModel)));
+    }
+    return canonical;
+}
+
+nn::GeneralResult<std::pair<sp<V1_3::IBuffer>, uint32_t>> allocate(
+        const nn::SharedDevice& device, const V1_3::BufferDesc& desc,
+        const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+        const hidl_vec<V1_3::BufferRole>& inputRoles,
+        const hidl_vec<V1_3::BufferRole>& outputRoles) {
+    auto nnDesc = NN_TRY(convertInput(desc));
+    auto nnPreparedModels = NN_TRY(downcastAll(preparedModels));
+    auto nnInputRoles = NN_TRY(convertInput(inputRoles));
+    auto nnOutputRoles = NN_TRY(convertInput(outputRoles));
+
+    auto buffer = NN_TRY(device->allocate(nnDesc, nnPreparedModels, nnInputRoles, nnOutputRoles));
+
+    const nn::Request::MemoryDomainToken token = buffer->getToken();
+    auto hidlBuffer = sp<Buffer>::make(std::move(buffer));
+    return std::make_pair(std::move(hidlBuffer), static_cast<uint32_t>(token));
+}
+
+}  // namespace
+
+Device::Device(nn::SharedDevice device, Executor executor)
+    : kDevice(std::move(device)), kExecutor(std::move(executor)) {
+    CHECK(kDevice != nullptr);
+    CHECK(kExecutor != nullptr);
+}
+
+Return<void> Device::getCapabilities(getCapabilities_cb cb) {
+    const auto capabilities = V1_0::utils::convert(kDevice->getCapabilities()).value();
+    cb(V1_0::ErrorStatus::NONE, capabilities);
+    return Void();
+}
+
+Return<void> Device::getCapabilities_1_1(getCapabilities_1_1_cb cb) {
+    const auto capabilities = V1_1::utils::convert(kDevice->getCapabilities()).value();
+    cb(V1_0::ErrorStatus::NONE, capabilities);
+    return Void();
+}
+
+Return<void> Device::getCapabilities_1_2(getCapabilities_1_2_cb cb) {
+    const auto capabilities = V1_2::utils::convert(kDevice->getCapabilities()).value();
+    cb(V1_0::ErrorStatus::NONE, capabilities);
+    return Void();
+}
+
+Return<void> Device::getCapabilities_1_3(getCapabilities_1_3_cb cb) {
+    const auto capabilities = V1_3::utils::convert(kDevice->getCapabilities()).value();
+    cb(V1_3::ErrorStatus::NONE, capabilities);
+    return Void();
+}
+
+Return<void> Device::getVersionString(getVersionString_cb cb) {
+    cb(V1_0::ErrorStatus::NONE, kDevice->getVersionString());
+    return Void();
+}
+
+Return<void> Device::getType(getType_cb cb) {
+    const auto maybeDeviceType = V1_2::utils::convert(kDevice->getType());
+    if (!maybeDeviceType.has_value()) {
+        const auto& [message, code] = maybeDeviceType.error();
+        LOG(ERROR) << "adapter::Device::getType failed with " << code << ": " << message;
+        cb(V1_2::utils::convert(code).value(), {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, maybeDeviceType.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getSupportedExtensions(getSupportedExtensions_cb cb) {
+    const auto maybeSupportedExtensions = V1_2::utils::convert(kDevice->getSupportedExtensions());
+    if (!maybeSupportedExtensions.has_value()) {
+        const auto& [message, code] = maybeSupportedExtensions.error();
+        LOG(ERROR) << "adapter::Device::getSupportedExtensions failed with " << code << ": "
+                   << message;
+        cb(V1_2::utils::convert(code).value(), {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, maybeSupportedExtensions.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getSupportedOperations(const V1_0::Model& model,
+                                            getSupportedOperations_cb cb) {
+    const auto result = adapter::getSupportedOperations(kDevice, model);
+    if (!result.has_value()) {
+        const auto& [message, code] = result.error();
+        LOG(ERROR) << "adapter::Device::getSupportedOperations_1_0 failed with " << code << ": "
+                   << message;
+        cb(V1_0::utils::convert(code).value(), {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, result.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_1(const V1_1::Model& model,
+                                                getSupportedOperations_1_1_cb cb) {
+    const auto result = adapter::getSupportedOperations(kDevice, model);
+    if (!result.has_value()) {
+        const auto& [message, code] = result.error();
+        LOG(ERROR) << "adapter::Device::getSupportedOperations_1_1 failed with " << code << ": "
+                   << message;
+        cb(V1_1::utils::convert(code).value(), {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, result.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_2(const V1_2::Model& model,
+                                                getSupportedOperations_1_2_cb cb) {
+    const auto result = adapter::getSupportedOperations(kDevice, model);
+    if (!result.has_value()) {
+        const auto& [message, code] = result.error();
+        LOG(ERROR) << "adapter::Device::getSupportedOperations_1_2 failed with " << code << ": "
+                   << message;
+        cb(V1_2::utils::convert(code).value(), {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, result.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_3(const V1_3::Model& model,
+                                                getSupportedOperations_1_3_cb cb) {
+    const auto result = adapter::getSupportedOperations(kDevice, model);
+    if (!result.has_value()) {
+        const auto& [message, code] = result.error();
+        LOG(ERROR) << "adapter::Device::getSupportedOperations_1_3 failed with " << code << ": "
+                   << message;
+        cb(V1_3::utils::convert(code).value(), {});
+    } else {
+        cb(V1_3::ErrorStatus::NONE, result.value());
+    }
+    return Void();
+}
+
+Return<void> Device::getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb) {
+    const auto [numModelCache, numDataCache] = kDevice->getNumberOfCacheFilesNeeded();
+    cb(V1_0::ErrorStatus::NONE, numModelCache, numDataCache);
+    return Void();
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel(const V1_0::Model& model,
+                                               const sp<V1_0::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModel(kDevice, kExecutor, model, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModel failed with " << code << ": " << message;
+        notify(callback.get(), code, nullptr);
+        return V1_0::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel_1_1(
+        const V1_1::Model& model, V1_1::ExecutionPreference preference,
+        const sp<V1_0::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModel_1_1(kDevice, kExecutor, model, preference, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModel_1_1 failed with " << code << ": " << message;
+        notify(callback.get(), code, nullptr);
+        return V1_1::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel_1_2(
+        const V1_2::Model& model, V1_1::ExecutionPreference preference,
+        const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+        const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModel_1_2(kDevice, kExecutor, model, preference, modelCache,
+                                            dataCache, token, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModel_1_2 failed with " << code << ": " << message;
+        notify(callback.get(), code, nullptr);
+        return V1_2::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Device::prepareModel_1_3(
+        const V1_3::Model& model, V1_1::ExecutionPreference preference, V1_3::Priority priority,
+        const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+        const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+        const sp<V1_3::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModel_1_3(kDevice, kExecutor, model, preference, priority,
+                                            deadline, modelCache, dataCache, token, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModel_1_3 failed with " << code << ": " << message;
+        notify(callback.get(), code, nullptr);
+        return V1_3::utils::convert(code).value();
+    }
+    return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModelFromCache(
+        const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+        const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModelFromCache(kDevice, kExecutor, modelCache, dataCache, token,
+                                                 callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModelFromCache failed with " << code << ": "
+                   << message;
+        notify(callback.get(), code, nullptr);
+        return V1_2::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Device::prepareModelFromCache_1_3(
+        const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+        const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+        const sp<V1_3::IPreparedModelCallback>& callback) {
+    auto result = adapter::prepareModelFromCache_1_3(kDevice, kExecutor, deadline, modelCache,
+                                                     dataCache, token, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::prepareModelFromCache_1_3 failed with " << code << ": "
+                   << message;
+        notify(callback.get(), code, nullptr);
+        return V1_3::utils::convert(code).value();
+    }
+    return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_0::DeviceStatus> Device::getStatus() {
+    return V1_0::DeviceStatus::AVAILABLE;
+}
+
+Return<void> Device::allocate(const V1_3::BufferDesc& desc,
+                              const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+                              const hidl_vec<V1_3::BufferRole>& inputRoles,
+                              const hidl_vec<V1_3::BufferRole>& outputRoles, allocate_cb cb) {
+    auto result = adapter::allocate(kDevice, desc, preparedModels, inputRoles, outputRoles);
+    if (!result.has_value()) {
+        const auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::Device::allocate failed with " << code << ": " << message;
+        cb(V1_3::utils::convert(code).value(), nullptr, /*token=*/0);
+        return Void();
+    }
+    auto [buffer, token] = std::move(result).value();
+    cb(V1_3::ErrorStatus::NONE, buffer, token);
+    return Void();
+}
+
+}  // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/PreparedModel.cpp b/neuralnetworks/utils/adapter/src/PreparedModel.cpp
new file mode 100644
index 0000000..8968c2c
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/PreparedModel.cpp
@@ -0,0 +1,417 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "PreparedModel.h"
+
+#include <ExecutionBurstServer.h>
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IFencedExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <hwbinder/IPCThreadState.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Utils.h>
+#include <nnapi/hal/1.2/Utils.h>
+#include <nnapi/hal/1.3/Conversions.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <nnapi/hal/HandleError.h>
+#include <sys/types.h>
+
+#include <memory>
+#include <thread>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+    auto result = nn::convert(object);
+    if (!result.has_value()) {
+        result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+    }
+    return result;
+}
+
+class FencedExecutionCallback final : public V1_3::IFencedExecutionCallback {
+  public:
+    explicit FencedExecutionCallback(const nn::ExecuteFencedInfoCallback& callback)
+        : kCallback(callback) {
+        CHECK(callback != nullptr);
+    }
+
+    Return<void> getExecutionInfo(getExecutionInfo_cb cb) override {
+        const auto result = kCallback();
+        if (!result.has_value()) {
+            const auto& [message, code] = result.error();
+            const auto status =
+                    V1_3::utils::convert(code).value_or(V1_3::ErrorStatus::GENERAL_FAILURE);
+            LOG(ERROR) << message;
+            cb(status, V1_2::utils::kNoTiming, V1_2::utils::kNoTiming);
+            return Void();
+        }
+        const auto [timingLaunched, timingFenced] = result.value();
+        const auto hidlTimingLaunched = V1_3::utils::convert(timingLaunched).value();
+        const auto hidlTimingFenced = V1_3::utils::convert(timingFenced).value();
+        cb(V1_3::ErrorStatus::NONE, hidlTimingLaunched, hidlTimingFenced);
+        return Void();
+    }
+
+  private:
+    const nn::ExecuteFencedInfoCallback kCallback;
+};
+
+using ExecutionResult = nn::ExecutionResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>;
+
+void notify(V1_0::IExecutionCallback* callback, nn::ErrorStatus status,
+            const std::vector<nn::OutputShape>& /*outputShapes*/, const nn::Timing& /*timing*/) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_0::utils::convert(status).value();
+        const auto ret = callback->notify(hidlStatus);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_0::IExecutionCallback::notify failed with " << ret.description();
+        }
+    }
+}
+
+void notify(V1_2::IExecutionCallback* callback, nn::ErrorStatus status,
+            const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_2::utils::convert(status).value();
+        const auto hidlOutputShapes = V1_2::utils::convert(outputShapes).value();
+        const auto hidlTiming = V1_2::utils::convert(timing).value();
+        const auto ret = callback->notify_1_2(hidlStatus, hidlOutputShapes, hidlTiming);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_2::IExecutionCallback::notify_1_2 failed with " << ret.description();
+        }
+    }
+}
+
+void notify(V1_3::IExecutionCallback* callback, nn::ErrorStatus status,
+            const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) {
+    if (callback != nullptr) {
+        const auto hidlStatus = V1_3::utils::convert(status).value();
+        const auto hidlOutputShapes = V1_3::utils::convert(outputShapes).value();
+        const auto hidlTiming = V1_3::utils::convert(timing).value();
+        const auto ret = callback->notify_1_3(hidlStatus, hidlOutputShapes, hidlTiming);
+        if (!ret.isOk()) {
+            LOG(ERROR) << "V1_3::IExecutionCallback::notify_1_3 failed with " << ret.description();
+        }
+    }
+}
+
+template <typename CallbackType>
+void notify(CallbackType* callback, ExecutionResult result) {
+    if (!result.has_value()) {
+        const auto [message, status, outputShapes] = std::move(result).error();
+        LOG(ERROR) << message;
+        notify(callback, status, outputShapes, {});
+    } else {
+        const auto [outputShapes, timing] = std::move(result).value();
+        notify(callback, nn::ErrorStatus::NONE, outputShapes, timing);
+    }
+}
+
+nn::GeneralResult<void> execute(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+                                const Executor& executor, const V1_0::Request& request,
+                                const sp<V1_0::IExecutionCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnRequest = NN_TRY(convertInput(request));
+
+    const std::any resource = preparedModel->getUnderlyingResource();
+    if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+        CHECK(*model != nullptr);
+        NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+                                         nn::ErrorStatus::INVALID_ARGUMENT));
+    }
+
+    Task task = [preparedModel, nnRequest = std::move(nnRequest), callback] {
+        auto result = preparedModel->execute(nnRequest, nn::MeasureTiming::NO, {}, {});
+        notify(callback.get(), std::move(result));
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> execute_1_2(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+                                    const Executor& executor, const V1_0::Request& request,
+                                    V1_2::MeasureTiming measure,
+                                    const sp<V1_2::IExecutionCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnRequest = NN_TRY(convertInput(request));
+    const auto nnMeasure = NN_TRY(convertInput(measure));
+
+    const std::any resource = preparedModel->getUnderlyingResource();
+    if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+        CHECK(*model != nullptr);
+        NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+                                         nn::ErrorStatus::INVALID_ARGUMENT));
+    }
+
+    Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, callback] {
+        auto result = preparedModel->execute(nnRequest, nnMeasure, {}, {});
+        notify(callback.get(), std::move(result));
+    };
+    executor(std::move(task), userId, {});
+
+    return {};
+}
+
+nn::GeneralResult<void> execute_1_3(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+                                    const Executor& executor, const V1_3::Request& request,
+                                    V1_2::MeasureTiming measure,
+                                    const V1_3::OptionalTimePoint& deadline,
+                                    const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+                                    const sp<V1_3::IExecutionCallback>& callback) {
+    if (callback.get() == nullptr) {
+        return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+    }
+
+    auto nnRequest = NN_TRY(convertInput(request));
+    const auto nnMeasure = NN_TRY(convertInput(measure));
+    const auto nnDeadline = NN_TRY(convertInput(deadline));
+    const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration));
+
+    const std::any resource = preparedModel->getUnderlyingResource();
+    if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+        CHECK(*model != nullptr);
+        NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+                                         nn::ErrorStatus::INVALID_ARGUMENT));
+    }
+
+    Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, nnDeadline,
+                 nnLoopTimeoutDuration, callback] {
+        auto result =
+                preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration);
+        notify(callback.get(), std::move(result));
+    };
+    executor(std::move(task), userId, nnDeadline);
+
+    return {};
+}
+
+nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously(
+        const nn::SharedPreparedModel& preparedModel, const V1_0::Request& request,
+        V1_2::MeasureTiming measure) {
+    const auto nnRequest = NN_TRY(utils::makeExecutionFailure(convertInput(request)));
+    const auto nnMeasure = NN_TRY(utils::makeExecutionFailure(convertInput(measure)));
+
+    const auto [outputShapes, timing] =
+            NN_TRY(preparedModel->execute(nnRequest, nnMeasure, {}, {}));
+
+    auto hidlOutputShapes = NN_TRY(utils::makeExecutionFailure(V1_2::utils::convert(outputShapes)));
+    const auto hidlTiming = NN_TRY(utils::makeExecutionFailure(V1_2::utils::convert(timing)));
+    return std::make_pair(std::move(hidlOutputShapes), hidlTiming);
+}
+
+nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously_1_3(
+        const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request,
+        V1_2::MeasureTiming measure, const V1_3::OptionalTimePoint& deadline,
+        const V1_3::OptionalTimeoutDuration& loopTimeoutDuration) {
+    const auto nnRequest = NN_TRY(utils::makeExecutionFailure(convertInput(request)));
+    const auto nnMeasure = NN_TRY(utils::makeExecutionFailure(convertInput(measure)));
+    const auto nnDeadline = NN_TRY(utils::makeExecutionFailure(convertInput(deadline)));
+    const auto nnLoopTimeoutDuration =
+            NN_TRY(utils::makeExecutionFailure(convertInput(loopTimeoutDuration)));
+
+    const auto [outputShapes, timing] =
+            NN_TRY(preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration));
+
+    auto hidlOutputShapes = NN_TRY(utils::makeExecutionFailure(V1_3::utils::convert(outputShapes)));
+    const auto hidlTiming = NN_TRY(utils::makeExecutionFailure(V1_3::utils::convert(timing)));
+    return std::make_pair(std::move(hidlOutputShapes), hidlTiming);
+}
+
+nn::GeneralResult<std::vector<nn::SyncFence>> convertSyncFences(
+        const hidl_vec<hidl_handle>& handles) {
+    std::vector<nn::SyncFence> syncFences;
+    syncFences.reserve(handles.size());
+    for (const auto& handle : handles) {
+        auto nativeHandle = NN_TRY(convertInput(handle));
+        auto syncFence = NN_TRY(utils::makeGeneralFailure(
+                nn::SyncFence::create(std::move(nativeHandle)), nn::ErrorStatus::INVALID_ARGUMENT));
+        syncFences.push_back(std::move(syncFence));
+    }
+    return syncFences;
+}
+
+nn::GeneralResult<std::pair<hidl_handle, sp<V1_3::IFencedExecutionCallback>>> executeFenced(
+        const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request,
+        const hidl_vec<hidl_handle>& waitFor, V1_2::MeasureTiming measure,
+        const V1_3::OptionalTimePoint& deadline,
+        const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+        const V1_3::OptionalTimeoutDuration& duration) {
+    const auto nnRequest = NN_TRY(convertInput(request));
+    const auto nnWaitFor = NN_TRY(convertSyncFences(waitFor));
+    const auto nnMeasure = NN_TRY(convertInput(measure));
+    const auto nnDeadline = NN_TRY(convertInput(deadline));
+    const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration));
+    const auto nnDuration = NN_TRY(convertInput(duration));
+
+    auto [syncFence, executeFencedCallback] = NN_TRY(preparedModel->executeFenced(
+            nnRequest, nnWaitFor, nnMeasure, nnDeadline, nnLoopTimeoutDuration, nnDuration));
+
+    auto hidlSyncFence = NN_TRY(V1_3::utils::convert(syncFence.getSharedHandle()));
+    auto hidlExecuteFencedCallback = sp<FencedExecutionCallback>::make(executeFencedCallback);
+    return std::make_pair(std::move(hidlSyncFence), std::move(hidlExecuteFencedCallback));
+}
+
+}  // namespace
+
+PreparedModel::PreparedModel(nn::SharedPreparedModel preparedModel, Executor executor, uid_t userId)
+    : kPreparedModel(std::move(preparedModel)), kExecutor(std::move(executor)), kUserId(userId) {
+    CHECK(kPreparedModel != nullptr);
+    CHECK(kExecutor != nullptr);
+}
+
+nn::SharedPreparedModel PreparedModel::getUnderlyingPreparedModel() const {
+    return kPreparedModel;
+}
+
+Return<V1_0::ErrorStatus> PreparedModel::execute(const V1_0::Request& request,
+                                                 const sp<V1_0::IExecutionCallback>& callback) {
+    auto result = adapter::execute(kPreparedModel, kUserId, kExecutor, request, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::execute failed with " << code << ": " << message;
+        notify(callback.get(), code, {}, {});
+        return V1_0::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> PreparedModel::execute_1_2(const V1_0::Request& request,
+                                                     V1_2::MeasureTiming measure,
+                                                     const sp<V1_2::IExecutionCallback>& callback) {
+    auto result =
+            adapter::execute_1_2(kPreparedModel, kUserId, kExecutor, request, measure, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::execute_1_2 failed with " << code << ": " << message;
+        notify(callback.get(), code, {}, {});
+        return V1_2::utils::convert(code).value();
+    }
+    return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> PreparedModel::execute_1_3(
+        const V1_3::Request& request, V1_2::MeasureTiming measure,
+        const V1_3::OptionalTimePoint& deadline,
+        const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+        const sp<V1_3::IExecutionCallback>& callback) {
+    auto result = adapter::execute_1_3(kPreparedModel, kUserId, kExecutor, request, measure,
+                                       deadline, loopTimeoutDuration, callback);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::execute_1_3 failed with " << code << ": " << message;
+        notify(callback.get(), code, {}, {});
+        return V1_3::utils::convert(code).value();
+    }
+    return V1_3::ErrorStatus::NONE;
+}
+
+Return<void> PreparedModel::executeSynchronously(const V1_0::Request& request,
+                                                 V1_2::MeasureTiming measure,
+                                                 executeSynchronously_cb cb) {
+    auto result = adapter::executeSynchronously(kPreparedModel, request, measure);
+    if (!result.has_value()) {
+        auto [message, code, outputShapes] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::executeSynchronously failed with " << code << ": "
+                   << message;
+        cb(V1_2::utils::convert(code).value(), V1_2::utils::convert(outputShapes).value(),
+           V1_2::utils::kNoTiming);
+        return Void();
+    }
+    auto [outputShapes, timing] = std::move(result).value();
+    cb(V1_0::ErrorStatus::NONE, outputShapes, timing);
+    return Void();
+}
+
+Return<void> PreparedModel::executeSynchronously_1_3(
+        const V1_3::Request& request, V1_2::MeasureTiming measure,
+        const V1_3::OptionalTimePoint& deadline,
+        const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, executeSynchronously_1_3_cb cb) {
+    auto result = adapter::executeSynchronously_1_3(kPreparedModel, request, measure, deadline,
+                                                    loopTimeoutDuration);
+    if (!result.has_value()) {
+        auto [message, code, outputShapes] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::executeSynchronously_1_3 failed with " << code
+                   << ": " << message;
+        cb(V1_3::utils::convert(code).value(), V1_3::utils::convert(outputShapes).value(),
+           V1_2::utils::kNoTiming);
+        return Void();
+    }
+    auto [outputShapes, timing] = std::move(result).value();
+    cb(V1_3::ErrorStatus::NONE, outputShapes, timing);
+    return Void();
+}
+
+Return<void> PreparedModel::configureExecutionBurst(
+        const sp<V1_2::IBurstCallback>& callback,
+        const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+        const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+        configureExecutionBurst_cb cb) {
+    const sp<V1_2::IBurstContext> burst = nn::ExecutionBurstServer::create(
+            callback, requestChannel, resultChannel, this, std::chrono::microseconds{0});
+
+    if (burst == nullptr) {
+        cb(V1_0::ErrorStatus::GENERAL_FAILURE, {});
+    } else {
+        cb(V1_0::ErrorStatus::NONE, burst);
+    }
+    return Void();
+}
+
+Return<void> PreparedModel::executeFenced(const V1_3::Request& request,
+                                          const hidl_vec<hidl_handle>& waitFor,
+                                          V1_2::MeasureTiming measure,
+                                          const V1_3::OptionalTimePoint& deadline,
+                                          const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+                                          const V1_3::OptionalTimeoutDuration& duration,
+                                          executeFenced_cb callback) {
+    auto result = adapter::executeFenced(kPreparedModel, request, waitFor, measure, deadline,
+                                         loopTimeoutDuration, duration);
+    if (!result.has_value()) {
+        auto [message, code] = std::move(result).error();
+        LOG(ERROR) << "adapter::PreparedModel::executeFenced failed with " << code << ": "
+                   << message;
+        callback(V1_3::utils::convert(code).value(), {}, nullptr);
+        return Void();
+    }
+    auto [syncFence, executeFencedCallback] = std::move(result).value();
+    callback(V1_3::ErrorStatus::NONE, syncFence, executeFencedCallback);
+    return Void();
+}
+
+}  // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/common/Android.bp b/neuralnetworks/utils/common/Android.bp
index 6c491ae..50295f1 100644
--- a/neuralnetworks/utils/common/Android.bp
+++ b/neuralnetworks/utils/common/Android.bp
@@ -22,10 +22,12 @@
     export_include_dirs: ["include"],
     cflags: ["-Wthread-safety"],
     static_libs: [
+        "libarect",
         "neuralnetworks_types",
     ],
     shared_libs: [
         "libhidlbase",
+        "libnativewindow",
     ],
 }
 
diff --git a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
index b13785d..2f6112a 100644
--- a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
+++ b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
@@ -71,13 +71,15 @@
 nn::GeneralResult<void> unflushDataFromSharedToPointer(
         const nn::Request& request, const std::optional<nn::Request>& maybeRequestInShared);
 
-std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
-                                             const std::vector<nn::Operation>& operations);
+nn::GeneralResult<std::vector<uint32_t>> countNumberOfConsumers(
+        size_t numberOfOperands, const std::vector<nn::Operation>& operations);
 
+nn::GeneralResult<hidl_memory> createHidlMemoryFromSharedMemory(const nn::SharedMemory& memory);
 nn::GeneralResult<nn::SharedMemory> createSharedMemoryFromHidlMemory(const hidl_memory& memory);
 
-nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::SharedHandle& handle);
-nn::GeneralResult<nn::SharedHandle> sharedHandleFromNativeHandle(const native_handle_t* handle);
+nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::Handle& handle);
+nn::GeneralResult<nn::Handle> sharedHandleFromNativeHandle(const native_handle_t* handle);
+
 nn::GeneralResult<hidl_vec<hidl_handle>> convertSyncFences(
         const std::vector<nn::SyncFence>& fences);
 
diff --git a/neuralnetworks/utils/common/src/CommonUtils.cpp b/neuralnetworks/utils/common/src/CommonUtils.cpp
index 9021563..924ecb2 100644
--- a/neuralnetworks/utils/common/src/CommonUtils.cpp
+++ b/neuralnetworks/utils/common/src/CommonUtils.cpp
@@ -20,11 +20,14 @@
 
 #include <android-base/logging.h>
 #include <android-base/unique_fd.h>
+#include <android/hardware_buffer.h>
+#include <hidl/HidlSupport.h>
 #include <nnapi/Result.h>
 #include <nnapi/SharedMemory.h>
 #include <nnapi/TypeUtils.h>
 #include <nnapi/Types.h>
 #include <nnapi/Validation.h>
+#include <vndk/hardware_buffer.h>
 
 #include <algorithm>
 #include <any>
@@ -243,49 +246,133 @@
     return {};
 }
 
-std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
-                                             const std::vector<nn::Operation>& operations) {
-    return nn::countNumberOfConsumers(numberOfOperands, operations);
+nn::GeneralResult<std::vector<uint32_t>> countNumberOfConsumers(
+        size_t numberOfOperands, const std::vector<nn::Operation>& operations) {
+    return makeGeneralFailure(nn::countNumberOfConsumers(numberOfOperands, operations));
 }
 
-nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::SharedHandle& handle) {
-    if (handle == nullptr) {
-        return {};
+nn::GeneralResult<hidl_memory> createHidlMemoryFromSharedMemory(const nn::SharedMemory& memory) {
+    if (memory == nullptr) {
+        return NN_ERROR() << "Memory must be non-empty";
+    }
+    if (const auto* handle = std::get_if<nn::Handle>(&memory->handle)) {
+        return hidl_memory(memory->name, NN_TRY(hidlHandleFromSharedHandle(*handle)), memory->size);
     }
 
+    const auto* ahwb = std::get<nn::HardwareBufferHandle>(memory->handle).get();
+    AHardwareBuffer_Desc bufferDesc;
+    AHardwareBuffer_describe(ahwb, &bufferDesc);
+
+    if (bufferDesc.format == AHARDWAREBUFFER_FORMAT_BLOB) {
+        CHECK_EQ(memory->size, bufferDesc.width);
+        CHECK_EQ(memory->name, "hardware_buffer_blob");
+    } else {
+        CHECK_EQ(memory->size, 0u);
+        CHECK_EQ(memory->name, "hardware_buffer");
+    }
+
+    const native_handle_t* nativeHandle = AHardwareBuffer_getNativeHandle(ahwb);
+    const hidl_handle hidlHandle(nativeHandle);
+    hidl_handle handle(hidlHandle);
+
+    return hidl_memory(memory->name, std::move(handle), memory->size);
+}
+
+static uint32_t roundUpToMultiple(uint32_t value, uint32_t multiple) {
+    return (value + multiple - 1) / multiple * multiple;
+}
+
+nn::GeneralResult<nn::SharedMemory> createSharedMemoryFromHidlMemory(const hidl_memory& memory) {
+    CHECK_LE(memory.size(), std::numeric_limits<uint32_t>::max());
+
+    if (memory.name() != "hardware_buffer_blob") {
+        return std::make_shared<const nn::Memory>(nn::Memory{
+                .handle = NN_TRY(sharedHandleFromNativeHandle(memory.handle())),
+                .size = static_cast<uint32_t>(memory.size()),
+                .name = memory.name(),
+        });
+    }
+
+    const auto size = memory.size();
+    const auto format = AHARDWAREBUFFER_FORMAT_BLOB;
+    const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
+    const uint32_t width = size;
+    const uint32_t height = 1;  // height is always 1 for BLOB mode AHardwareBuffer.
+    const uint32_t layers = 1;  // layers is always 1 for BLOB mode AHardwareBuffer.
+
+    // AHardwareBuffer_createFromHandle() might fail because an allocator
+    // expects a specific stride value. In that case, we try to guess it by
+    // aligning the width to small powers of 2.
+    // TODO(b/174120849): Avoid stride assumptions.
+    AHardwareBuffer* hardwareBuffer = nullptr;
+    status_t status = UNKNOWN_ERROR;
+    for (uint32_t alignment : {1, 4, 32, 64, 128, 2, 8, 16}) {
+        const uint32_t stride = roundUpToMultiple(width, alignment);
+        AHardwareBuffer_Desc desc{
+                .width = width,
+                .height = height,
+                .layers = layers,
+                .format = format,
+                .usage = usage,
+                .stride = stride,
+        };
+        status = AHardwareBuffer_createFromHandle(&desc, memory.handle(),
+                                                  AHARDWAREBUFFER_CREATE_FROM_HANDLE_METHOD_CLONE,
+                                                  &hardwareBuffer);
+        if (status == NO_ERROR) {
+            break;
+        }
+    }
+    if (status != NO_ERROR) {
+        return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+               << "Can't create AHardwareBuffer from handle. Error: " << status;
+    }
+
+    return std::make_shared<const nn::Memory>(nn::Memory{
+            .handle = nn::HardwareBufferHandle(hardwareBuffer, /*takeOwnership=*/true),
+            .size = static_cast<uint32_t>(memory.size()),
+            .name = memory.name(),
+    });
+}
+
+nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::Handle& handle) {
     std::vector<base::unique_fd> fds;
-    fds.reserve(handle->fds.size());
-    for (const auto& fd : handle->fds) {
-        int dupFd = dup(fd);
+    fds.reserve(handle.fds.size());
+    for (const auto& fd : handle.fds) {
+        const int dupFd = dup(fd);
         if (dupFd == -1) {
             return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
         }
         fds.emplace_back(dupFd);
     }
 
-    native_handle_t* nativeHandle = native_handle_create(handle->fds.size(), handle->ints.size());
+    constexpr size_t kIntMax = std::numeric_limits<int>::max();
+    CHECK_LE(handle.fds.size(), kIntMax);
+    CHECK_LE(handle.ints.size(), kIntMax);
+    native_handle_t* nativeHandle = native_handle_create(static_cast<int>(handle.fds.size()),
+                                                         static_cast<int>(handle.ints.size()));
     if (nativeHandle == nullptr) {
         return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to create native_handle";
     }
     for (size_t i = 0; i < fds.size(); ++i) {
         nativeHandle->data[i] = fds[i].release();
     }
-    std::copy(handle->ints.begin(), handle->ints.end(), &nativeHandle->data[nativeHandle->numFds]);
+    std::copy(handle.ints.begin(), handle.ints.end(), &nativeHandle->data[nativeHandle->numFds]);
 
     hidl_handle hidlHandle;
     hidlHandle.setTo(nativeHandle, /*shouldOwn=*/true);
     return hidlHandle;
 }
 
-nn::GeneralResult<nn::SharedHandle> sharedHandleFromNativeHandle(const native_handle_t* handle) {
+nn::GeneralResult<nn::Handle> sharedHandleFromNativeHandle(const native_handle_t* handle) {
     if (handle == nullptr) {
-        return nullptr;
+        return NN_ERROR() << "sharedHandleFromNativeHandle failed because handle is nullptr";
     }
 
     std::vector<base::unique_fd> fds;
     fds.reserve(handle->numFds);
     for (int i = 0; i < handle->numFds; ++i) {
-        int dupFd = dup(handle->data[i]);
+        const int dupFd = dup(handle->data[i]);
         if (dupFd == -1) {
             return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
         }
@@ -295,18 +382,18 @@
     std::vector<int> ints(&handle->data[handle->numFds],
                           &handle->data[handle->numFds + handle->numInts]);
 
-    return std::make_shared<const nn::Handle>(nn::Handle{
-            .fds = std::move(fds),
-            .ints = std::move(ints),
-    });
+    return nn::Handle{.fds = std::move(fds), .ints = std::move(ints)};
 }
 
 nn::GeneralResult<hidl_vec<hidl_handle>> convertSyncFences(
         const std::vector<nn::SyncFence>& syncFences) {
     hidl_vec<hidl_handle> handles(syncFences.size());
     for (size_t i = 0; i < syncFences.size(); ++i) {
-        handles[i] =
-                NN_TRY(hal::utils::hidlHandleFromSharedHandle(syncFences[i].getSharedHandle()));
+        const auto& handle = syncFences[i].getSharedHandle();
+        if (handle == nullptr) {
+            return NN_ERROR() << "convertSyncFences failed because sync fence is empty";
+        }
+        handles[i] = NN_TRY(hidlHandleFromSharedHandle(*handle));
     }
     return handles;
 }