diff --git a/neuralnetworks/1.0/utils/Android.bp b/neuralnetworks/1.0/utils/Android.bp
new file mode 100644
index 0000000..57a052f
--- /dev/null
+++ b/neuralnetworks/1.0/utils/Android.bp
@@ -0,0 +1,33 @@
+//
+// 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_1_0",
+    defaults: ["neuralnetworks_utils_defaults"],
+    srcs: ["src/*"],
+    local_include_dirs: ["include/nnapi/hal/1.0/"],
+    export_include_dirs: ["include"],
+    static_libs: [
+        "neuralnetworks_types",
+        "neuralnetworks_utils_hal_common",
+    ],
+    shared_libs: [
+        "android.hardware.neuralnetworks@1.0",
+    ],
+    export_static_lib_headers: [
+        "neuralnetworks_utils_hal_common",
+    ],
+}
diff --git a/neuralnetworks/1.0/utils/OWNERS b/neuralnetworks/1.0/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.0/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h
new file mode 100644
index 0000000..8ad98cb
--- /dev/null
+++ b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h
@@ -0,0 +1,66 @@
+/*
+ * 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_1_0_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperandType> convert(const hal::V1_0::OperandType& operandType);
+Result<OperationType> convert(const hal::V1_0::OperationType& operationType);
+Result<Operand::LifeTime> convert(const hal::V1_0::OperandLifeTime& lifetime);
+Result<DeviceStatus> convert(const hal::V1_0::DeviceStatus& deviceStatus);
+Result<Capabilities::PerformanceInfo> convert(const hal::V1_0::PerformanceInfo& performanceInfo);
+Result<Capabilities> convert(const hal::V1_0::Capabilities& capabilities);
+Result<DataLocation> convert(const hal::V1_0::DataLocation& location);
+Result<Operand> convert(const hal::V1_0::Operand& operand);
+Result<Operation> convert(const hal::V1_0::Operation& operation);
+Result<Model::OperandValues> convert(const hardware::hidl_vec<uint8_t>& operandValues);
+Result<Memory> convert(const hardware::hidl_memory& memory);
+Result<Model> convert(const hal::V1_0::Model& model);
+Result<Request::Argument> convert(const hal::V1_0::RequestArgument& requestArgument);
+Result<Request> convert(const hal::V1_0::Request& request);
+Result<ErrorStatus> convert(const hal::V1_0::ErrorStatus& status);
+
+}  // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType);
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime);
+nn::Result<DeviceStatus> convert(const nn::DeviceStatus& deviceStatus);
+nn::Result<PerformanceInfo> convert(const nn::Capabilities::PerformanceInfo& performanceInfo);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<DataLocation> convert(const nn::DataLocation& location);
+nn::Result<Operand> convert(const nn::Operand& operand);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues);
+nn::Result<hidl_memory> convert(const nn::Memory& memory);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<RequestArgument> convert(const nn::Request::Argument& requestArgument);
+nn::Result<hidl_memory> convert(const nn::Request::MemoryPool& memoryPool);
+nn::Result<Request> convert(const nn::Request& request);
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& status);
+
+}  // namespace android::hardware::neuralnetworks::V1_0::utils
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_CONVERSIONS_H
diff --git a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
new file mode 100644
index 0000000..ec8da06
--- /dev/null
+++ b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
@@ -0,0 +1,63 @@
+/*
+ * 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_1_0_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
+
+#include "nnapi/hal/1.0/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+
+constexpr auto kVersion = nn::Version::ANDROID_OC_MR1;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+    const auto canonical = NN_TRY(nn::convert(halObject));
+    const auto version = NN_TRY(nn::validate(canonical));
+    if (version > utils::kVersion) {
+        return NN_ERROR() << "";
+    }
+    return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+    const auto result = utils::validate(halObject);
+    if (!result.has_value()) {
+        LOG(ERROR) << result.error();
+    }
+    return result.has_value();
+}
+
+template <typename Type>
+decltype(nn::convert(std::declval<Type>())) validatedConvertToCanonical(const Type& halObject) {
+    auto canonical = NN_TRY(nn::convert(halObject));
+    const auto version = NN_TRY(nn::validate(canonical));
+    if (version > utils::kVersion) {
+        return NN_ERROR() << "";
+    }
+    return canonical;
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_0::utils
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
diff --git a/neuralnetworks/1.0/utils/src/Assertions.cpp b/neuralnetworks/1.0/utils/src/Assertions.cpp
new file mode 100644
index 0000000..0f00951
--- /dev/null
+++ b/neuralnetworks/1.0/utils/src/Assertions.cpp
@@ -0,0 +1,122 @@
+/*
+ * 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 <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(lhsType, rhsType)                                                   \
+    static_assert(                                                                              \
+            std::is_same_v<                                                                     \
+                    std::underlying_type_t<::android::hardware::neuralnetworks::V1_0::lhsType>, \
+                    std::underlying_type_t<::android::nn::rhsType>>,                            \
+            "::android::hardware::neuralnetworks::V1_0::" #lhsType                              \
+            " does not have the same underlying type as ::android::nn::" #rhsType)
+
+COMPARE_ENUMS_TYPES(OperandType, OperandType);
+COMPARE_ENUMS_TYPES(OperationType, OperationType);
+COMPARE_ENUMS_TYPES(ErrorStatus, ErrorStatus);
+COMPARE_ENUMS_TYPES(OperandLifeTime, Operand::LifeTime);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, lhsType, rhsType)                               \
+    static_assert(                                                                               \
+            static_cast<                                                                         \
+                    std::underlying_type_t<::android::hardware::neuralnetworks::V1_0::lhsType>>( \
+                    ::android::hardware::neuralnetworks::V1_0::lhsType::lhsSymbol) ==            \
+                    static_cast<std::underlying_type_t<::android::nn::rhsType>>(                 \
+                            ::android::nn::rhsType::rhsSymbol),                                  \
+            "::android::hardware::neuralnetworks::V1_0::" #lhsType "::" #lhsSymbol               \
+            " does not match ::android::nn::" #rhsType "::" #rhsSymbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperandType, OperandType)
+
+COMPARE_ENUMS(FLOAT32);
+COMPARE_ENUMS(INT32);
+COMPARE_ENUMS(UINT32);
+COMPARE_ENUMS(TENSOR_FLOAT32);
+COMPARE_ENUMS(TENSOR_INT32);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM);
+COMPARE_ENUMS(OEM);
+COMPARE_ENUMS(TENSOR_OEM_BYTE);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperationType, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, ErrorStatus, ErrorStatus)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(DEVICE_UNAVAILABLE);
+COMPARE_ENUMS(GENERAL_FAILURE);
+COMPARE_ENUMS(OUTPUT_INSUFFICIENT_SIZE);
+COMPARE_ENUMS(INVALID_ARGUMENT);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(lhsSymbol, rhsSymbol) \
+    COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, OperandLifeTime, Operand::LifeTime)
+
+COMPARE_ENUMS(TEMPORARY_VARIABLE, TEMPORARY_VARIABLE);
+COMPARE_ENUMS(MODEL_INPUT, SUBGRAPH_INPUT);
+COMPARE_ENUMS(MODEL_OUTPUT, SUBGRAPH_OUTPUT);
+COMPARE_ENUMS(CONSTANT_COPY, CONSTANT_COPY);
+COMPARE_ENUMS(CONSTANT_REFERENCE, CONSTANT_REFERENCE);
+COMPARE_ENUMS(NO_VALUE, NO_VALUE);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+}  // anonymous namespace
diff --git a/neuralnetworks/1.0/utils/src/Conversions.cpp b/neuralnetworks/1.0/utils/src/Conversions.cpp
new file mode 100644
index 0000000..4a58f3b
--- /dev/null
+++ b/neuralnetworks/1.0/utils/src/Conversions.cpp
@@ -0,0 +1,361 @@
+/*
+ * 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 "Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <algorithm>
+#include <functional>
+#include <iterator>
+#include <memory>
+#include <type_traits>
+#include <utility>
+#include <variant>
+
+namespace {
+
+template <typename Type>
+constexpr std::underlying_type_t<Type> underlyingType(Type value) {
+    return static_cast<std::underlying_type_t<Type>>(value);
+}
+
+}  // namespace
+
+namespace android::nn {
+namespace {
+
+using hardware::hidl_memory;
+using hardware::hidl_vec;
+
+template <typename Input>
+using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;
+
+template <typename Type>
+Result<std::vector<ConvertOutput<Type>>> convert(const hidl_vec<Type>& arguments) {
+    std::vector<ConvertOutput<Type>> canonical;
+    canonical.reserve(arguments.size());
+    for (const auto& argument : arguments) {
+        canonical.push_back(NN_TRY(nn::convert(argument)));
+    }
+    return canonical;
+}
+
+}  // anonymous namespace
+
+Result<OperandType> convert(const hal::V1_0::OperandType& operandType) {
+    return static_cast<OperandType>(operandType);
+}
+
+Result<OperationType> convert(const hal::V1_0::OperationType& operationType) {
+    return static_cast<OperationType>(operationType);
+}
+
+Result<Operand::LifeTime> convert(const hal::V1_0::OperandLifeTime& lifetime) {
+    return static_cast<Operand::LifeTime>(lifetime);
+}
+
+Result<DeviceStatus> convert(const hal::V1_0::DeviceStatus& deviceStatus) {
+    return static_cast<DeviceStatus>(deviceStatus);
+}
+
+Result<Capabilities::PerformanceInfo> convert(const hal::V1_0::PerformanceInfo& performanceInfo) {
+    return Capabilities::PerformanceInfo{
+            .execTime = performanceInfo.execTime,
+            .powerUsage = performanceInfo.powerUsage,
+    };
+}
+
+Result<Capabilities> convert(const hal::V1_0::Capabilities& capabilities) {
+    const auto quantized8Performance = NN_TRY(convert(capabilities.quantized8Performance));
+    const auto float32Performance = NN_TRY(convert(capabilities.float32Performance));
+
+    auto table = hal::utils::makeQuantized8PerformanceConsistentWithP(float32Performance,
+                                                                      quantized8Performance);
+
+    return Capabilities{
+            .relaxedFloat32toFloat16PerformanceScalar = float32Performance,
+            .relaxedFloat32toFloat16PerformanceTensor = float32Performance,
+            .operandPerformance = std::move(table),
+    };
+}
+
+Result<DataLocation> convert(const hal::V1_0::DataLocation& location) {
+    return DataLocation{
+            .poolIndex = location.poolIndex,
+            .offset = location.offset,
+            .length = location.length,
+    };
+}
+
+Result<Operand> convert(const hal::V1_0::Operand& operand) {
+    return Operand{
+            .type = NN_TRY(convert(operand.type)),
+            .dimensions = operand.dimensions,
+            .scale = operand.scale,
+            .zeroPoint = operand.zeroPoint,
+            .lifetime = NN_TRY(convert(operand.lifetime)),
+            .location = NN_TRY(convert(operand.location)),
+    };
+}
+
+Result<Operation> convert(const hal::V1_0::Operation& operation) {
+    return Operation{
+            .type = NN_TRY(convert(operation.type)),
+            .inputs = operation.inputs,
+            .outputs = operation.outputs,
+    };
+}
+
+Result<Model::OperandValues> convert(const hidl_vec<uint8_t>& operandValues) {
+    return Model::OperandValues(operandValues.data(), operandValues.size());
+}
+
+Result<Memory> convert(const hidl_memory& memory) {
+    return createSharedMemoryFromHidlMemory(memory);
+}
+
+Result<Model> convert(const hal::V1_0::Model& model) {
+    auto operations = NN_TRY(convert(model.operations));
+
+    // Verify number of consumers.
+    const auto numberOfConsumers =
+            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]) {
+            return NN_ERROR() << "Invalid numberOfConsumers for operand " << i << ", expected "
+                              << numberOfConsumers[i] << " but found "
+                              << model.operands[i].numberOfConsumers;
+        }
+    }
+
+    auto main = Model::Subgraph{
+            .operands = NN_TRY(convert(model.operands)),
+            .operations = std::move(operations),
+            .inputIndexes = model.inputIndexes,
+            .outputIndexes = model.outputIndexes,
+    };
+
+    return Model{
+            .main = std::move(main),
+            .operandValues = NN_TRY(convert(model.operandValues)),
+            .pools = NN_TRY(convert(model.pools)),
+    };
+}
+
+Result<Request::Argument> convert(const hal::V1_0::RequestArgument& argument) {
+    const auto lifetime = argument.hasNoValue ? Request::Argument::LifeTime::NO_VALUE
+                                              : Request::Argument::LifeTime::POOL;
+    return Request::Argument{
+            .lifetime = lifetime,
+            .location = NN_TRY(convert(argument.location)),
+            .dimensions = argument.dimensions,
+    };
+}
+
+Result<Request> convert(const hal::V1_0::Request& request) {
+    auto memories = NN_TRY(convert(request.pools));
+    std::vector<Request::MemoryPool> pools;
+    pools.reserve(memories.size());
+    std::move(memories.begin(), memories.end(), std::back_inserter(pools));
+
+    return Request{
+            .inputs = NN_TRY(convert(request.inputs)),
+            .outputs = NN_TRY(convert(request.outputs)),
+            .pools = std::move(pools),
+    };
+}
+
+Result<ErrorStatus> convert(const hal::V1_0::ErrorStatus& status) {
+    switch (status) {
+        case hal::V1_0::ErrorStatus::NONE:
+        case hal::V1_0::ErrorStatus::DEVICE_UNAVAILABLE:
+        case hal::V1_0::ErrorStatus::GENERAL_FAILURE:
+        case hal::V1_0::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+        case hal::V1_0::ErrorStatus::INVALID_ARGUMENT:
+            return static_cast<ErrorStatus>(status);
+    }
+    return NN_ERROR() << "Invalid ErrorStatus " << underlyingType(status);
+}
+
+}  // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+namespace {
+
+template <typename Input>
+using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;
+
+template <typename Type>
+nn::Result<hidl_vec<ConvertOutput<Type>>> convert(const std::vector<Type>& arguments) {
+    hidl_vec<ConvertOutput<Type>> halObject(arguments.size());
+    for (size_t i = 0; i < arguments.size(); ++i) {
+        halObject[i] = NN_TRY(utils::convert(arguments[i]));
+    }
+    return halObject;
+}
+
+}  // anonymous namespace
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType) {
+    return static_cast<OperandType>(operandType);
+}
+
+nn::Result<OperationType> convert(const nn::OperationType& operationType) {
+    return static_cast<OperationType>(operationType);
+}
+
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime) {
+    if (lifetime == nn::Operand::LifeTime::POINTER) {
+        return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+    }
+    return static_cast<OperandLifeTime>(lifetime);
+}
+
+nn::Result<DeviceStatus> convert(const nn::DeviceStatus& deviceStatus) {
+    return static_cast<DeviceStatus>(deviceStatus);
+}
+
+nn::Result<PerformanceInfo> convert(const nn::Capabilities::PerformanceInfo& performanceInfo) {
+    return PerformanceInfo{
+            .execTime = performanceInfo.execTime,
+            .powerUsage = performanceInfo.powerUsage,
+    };
+}
+
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
+    return Capabilities{
+            .float32Performance = NN_TRY(convert(
+                    capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_FLOAT32))),
+            .quantized8Performance = NN_TRY(convert(
+                    capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_QUANT8_ASYMM))),
+    };
+}
+
+nn::Result<DataLocation> convert(const nn::DataLocation& location) {
+    return DataLocation{
+            .poolIndex = location.poolIndex,
+            .offset = location.offset,
+            .length = location.length,
+    };
+}
+
+nn::Result<Operand> convert(const nn::Operand& operand) {
+    return Operand{
+            .type = NN_TRY(convert(operand.type)),
+            .dimensions = operand.dimensions,
+            .numberOfConsumers = 0,
+            .scale = operand.scale,
+            .zeroPoint = operand.zeroPoint,
+            .lifetime = NN_TRY(convert(operand.lifetime)),
+            .location = NN_TRY(convert(operand.location)),
+    };
+}
+
+nn::Result<Operation> convert(const nn::Operation& operation) {
+    return Operation{
+            .type = NN_TRY(convert(operation.type)),
+            .inputs = operation.inputs,
+            .outputs = operation.outputs,
+    };
+}
+
+nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues) {
+    return hidl_vec<uint8_t>(operandValues.data(), operandValues.data() + operandValues.size());
+}
+
+nn::Result<hidl_memory> convert(const nn::Memory& memory) {
+    const auto hidlMemory = hidl_memory(memory.name, memory.handle->handle(), memory.size);
+    // Copy memory to force the native_handle_t to be copied.
+    auto copiedMemory = hidlMemory;
+    return copiedMemory;
+}
+
+nn::Result<Model> convert(const nn::Model& model) {
+    if (!hal::utils::hasNoPointerData(model)) {
+        return NN_ERROR() << "Mdoel cannot be converted because it contains pointer-based memory";
+    }
+
+    auto operands = NN_TRY(convert(model.main.operands));
+
+    // Update number of consumers.
+    const auto numberOfConsumers =
+            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];
+    }
+
+    return Model{
+            .operands = std::move(operands),
+            .operations = NN_TRY(convert(model.main.operations)),
+            .inputIndexes = model.main.inputIndexes,
+            .outputIndexes = model.main.outputIndexes,
+            .operandValues = NN_TRY(convert(model.operandValues)),
+            .pools = NN_TRY(convert(model.pools)),
+    };
+}
+
+nn::Result<RequestArgument> convert(const nn::Request::Argument& requestArgument) {
+    if (requestArgument.lifetime == nn::Request::Argument::LifeTime::POINTER) {
+        return NN_ERROR() << "Request cannot be converted because it contains pointer-based memory";
+    }
+    const bool hasNoValue = requestArgument.lifetime == nn::Request::Argument::LifeTime::NO_VALUE;
+    return RequestArgument{
+            .hasNoValue = hasNoValue,
+            .location = NN_TRY(convert(requestArgument.location)),
+            .dimensions = requestArgument.dimensions,
+    };
+}
+
+nn::Result<hidl_memory> convert(const nn::Request::MemoryPool& memoryPool) {
+    return convert(std::get<nn::Memory>(memoryPool));
+}
+
+nn::Result<Request> convert(const nn::Request& request) {
+    if (!hal::utils::hasNoPointerData(request)) {
+        return NN_ERROR() << "Request cannot be converted because it contains pointer-based memory";
+    }
+
+    return Request{
+            .inputs = NN_TRY(convert(request.inputs)),
+            .outputs = NN_TRY(convert(request.outputs)),
+            .pools = NN_TRY(convert(request.pools)),
+    };
+}
+
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& status) {
+    switch (status) {
+        case nn::ErrorStatus::NONE:
+        case nn::ErrorStatus::DEVICE_UNAVAILABLE:
+        case nn::ErrorStatus::GENERAL_FAILURE:
+        case nn::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+        case nn::ErrorStatus::INVALID_ARGUMENT:
+            return static_cast<ErrorStatus>(status);
+        default:
+            return ErrorStatus::GENERAL_FAILURE;
+    }
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_0::utils