diff --git a/neuralnetworks/1.3/utils/Android.bp b/neuralnetworks/1.3/utils/Android.bp
new file mode 100644
index 0000000..279b250
--- /dev/null
+++ b/neuralnetworks/1.3/utils/Android.bp
@@ -0,0 +1,39 @@
+//
+// 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_3",
+    defaults: ["neuralnetworks_utils_defaults"],
+    srcs: ["src/*"],
+    local_include_dirs: ["include/nnapi/hal/1.3/"],
+    export_include_dirs: ["include"],
+    static_libs: [
+        "neuralnetworks_types",
+        "neuralnetworks_utils_hal_common",
+        "neuralnetworks_utils_hal_1_0",
+        "neuralnetworks_utils_hal_1_1",
+        "neuralnetworks_utils_hal_1_2",
+    ],
+    shared_libs: [
+        "android.hardware.neuralnetworks@1.0",
+        "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.2",
+        "android.hardware.neuralnetworks@1.3",
+    ],
+    export_static_lib_headers: [
+        "neuralnetworks_utils_hal_common",
+    ],
+}
diff --git a/neuralnetworks/1.3/utils/OWNERS b/neuralnetworks/1.3/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.3/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/1.3/utils/include/nnapi/hal/1.3/Conversions.h b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Conversions.h
new file mode 100644
index 0000000..43987a9
--- /dev/null
+++ b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Conversions.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_1_3_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperandType> convert(const hal::V1_3::OperandType& operandType);
+Result<OperationType> convert(const hal::V1_3::OperationType& operationType);
+Result<Priority> convert(const hal::V1_3::Priority& priority);
+Result<Capabilities> convert(const hal::V1_3::Capabilities& capabilities);
+Result<Capabilities::OperandPerformance> convert(
+        const hal::V1_3::Capabilities::OperandPerformance& operandPerformance);
+Result<Operation> convert(const hal::V1_3::Operation& operation);
+Result<Operand::LifeTime> convert(const hal::V1_3::OperandLifeTime& operandLifeTime);
+Result<Operand> convert(const hal::V1_3::Operand& operand);
+Result<Model> convert(const hal::V1_3::Model& model);
+Result<Model::Subgraph> convert(const hal::V1_3::Subgraph& subgraph);
+Result<BufferDesc> convert(const hal::V1_3::BufferDesc& bufferDesc);
+Result<BufferRole> convert(const hal::V1_3::BufferRole& bufferRole);
+Result<Request> convert(const hal::V1_3::Request& request);
+Result<Request::MemoryPool> convert(const hal::V1_3::Request::MemoryPool& memoryPool);
+Result<OptionalTimePoint> convert(const hal::V1_3::OptionalTimePoint& optionalTimePoint);
+Result<OptionalTimeoutDuration> convert(
+        const hal::V1_3::OptionalTimeoutDuration& optionalTimeoutDuration);
+Result<ErrorStatus> convert(const hal::V1_3::ErrorStatus& errorStatus);
+
+Result<std::vector<BufferRole>> convert(
+        const hardware::hidl_vec<hal::V1_3::BufferRole>& bufferRoles);
+
+}  // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType);
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<Priority> convert(const nn::Priority& priority);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<Capabilities::OperandPerformance> convert(
+        const nn::Capabilities::OperandPerformance& operandPerformance);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& operandLifeTime);
+nn::Result<Operand> convert(const nn::Operand& operand);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<Subgraph> convert(const nn::Model::Subgraph& subgraph);
+nn::Result<BufferDesc> convert(const nn::BufferDesc& bufferDesc);
+nn::Result<BufferRole> convert(const nn::BufferRole& bufferRole);
+nn::Result<Request> convert(const nn::Request& request);
+nn::Result<Request::MemoryPool> convert(const nn::Request::MemoryPool& memoryPool);
+nn::Result<OptionalTimePoint> convert(const nn::OptionalTimePoint& optionalTimePoint);
+nn::Result<OptionalTimeoutDuration> convert(
+        const nn::OptionalTimeoutDuration& optionalTimeoutDuration);
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& errorStatus);
+
+nn::Result<hidl_vec<BufferRole>> convert(const std::vector<nn::BufferRole>& bufferRoles);
+
+}  // namespace android::hardware::neuralnetworks::V1_3::utils
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_CONVERSIONS_H
diff --git a/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
new file mode 100644
index 0000000..f8c975d
--- /dev/null
+++ b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
@@ -0,0 +1,67 @@
+/*
+ * 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_3_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
+
+#include "nnapi/hal/1.3/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.1/Conversions.h>
+#include <nnapi/hal/1.2/Conversions.h>
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+
+constexpr auto kDefaultPriority = Priority::MEDIUM;
+constexpr auto kVersion = nn::Version::ANDROID_R;
+
+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_3::utils
+
+#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
diff --git a/neuralnetworks/1.3/utils/src/Assertions.cpp b/neuralnetworks/1.3/utils/src/Assertions.cpp
new file mode 100644
index 0000000..96d647a
--- /dev/null
+++ b/neuralnetworks/1.3/utils/src/Assertions.cpp
@@ -0,0 +1,218 @@
+/*
+ * 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/1.3/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_3::lhsType>, \
+                    std::underlying_type_t<::android::nn::rhsType>>,                            \
+            "::android::hardware::neuralnetworks::V1_3::" #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(Priority, Priority);
+COMPARE_ENUMS_TYPES(OperandLifeTime, Operand::LifeTime);
+COMPARE_ENUMS_TYPES(ErrorStatus, ErrorStatus);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(symbol, lhsType, rhsType)                                             \
+    static_assert(                                                                               \
+            static_cast<                                                                         \
+                    std::underlying_type_t<::android::hardware::neuralnetworks::V1_3::lhsType>>( \
+                    ::android::hardware::neuralnetworks::V1_3::lhsType::symbol) ==               \
+                    static_cast<std::underlying_type_t<::android::nn::rhsType>>(                 \
+                            ::android::nn::rhsType::symbol),                                     \
+            "::android::hardware::neuralnetworks::V1_3::" #lhsType "::" #symbol                  \
+            " does not match ::android::nn::" #rhsType "::" #symbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(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(BOOL);
+COMPARE_ENUMS(TENSOR_QUANT16_SYMM);
+COMPARE_ENUMS(TENSOR_FLOAT16);
+COMPARE_ENUMS(TENSOR_BOOL8);
+COMPARE_ENUMS(FLOAT16);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM_PER_CHANNEL);
+COMPARE_ENUMS(TENSOR_QUANT16_ASYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM_SIGNED);
+COMPARE_ENUMS(SUBGRAPH);
+COMPARE_ENUMS(OEM);
+COMPARE_ENUMS(TENSOR_OEM_BYTE);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(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(BATCH_TO_SPACE_ND);
+COMPARE_ENUMS(DIV);
+COMPARE_ENUMS(MEAN);
+COMPARE_ENUMS(PAD);
+COMPARE_ENUMS(SPACE_TO_BATCH_ND);
+COMPARE_ENUMS(SQUEEZE);
+COMPARE_ENUMS(STRIDED_SLICE);
+COMPARE_ENUMS(SUB);
+COMPARE_ENUMS(TRANSPOSE);
+COMPARE_ENUMS(ABS);
+COMPARE_ENUMS(ARGMAX);
+COMPARE_ENUMS(ARGMIN);
+COMPARE_ENUMS(AXIS_ALIGNED_BBOX_TRANSFORM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(BOX_WITH_NMS_LIMIT);
+COMPARE_ENUMS(CAST);
+COMPARE_ENUMS(CHANNEL_SHUFFLE);
+COMPARE_ENUMS(DETECTION_POSTPROCESSING);
+COMPARE_ENUMS(EQUAL);
+COMPARE_ENUMS(EXP);
+COMPARE_ENUMS(EXPAND_DIMS);
+COMPARE_ENUMS(GATHER);
+COMPARE_ENUMS(GENERATE_PROPOSALS);
+COMPARE_ENUMS(GREATER);
+COMPARE_ENUMS(GREATER_EQUAL);
+COMPARE_ENUMS(GROUPED_CONV_2D);
+COMPARE_ENUMS(HEATMAP_MAX_KEYPOINT);
+COMPARE_ENUMS(INSTANCE_NORMALIZATION);
+COMPARE_ENUMS(LESS);
+COMPARE_ENUMS(LESS_EQUAL);
+COMPARE_ENUMS(LOG);
+COMPARE_ENUMS(LOGICAL_AND);
+COMPARE_ENUMS(LOGICAL_NOT);
+COMPARE_ENUMS(LOGICAL_OR);
+COMPARE_ENUMS(LOG_SOFTMAX);
+COMPARE_ENUMS(MAXIMUM);
+COMPARE_ENUMS(MINIMUM);
+COMPARE_ENUMS(NEG);
+COMPARE_ENUMS(NOT_EQUAL);
+COMPARE_ENUMS(PAD_V2);
+COMPARE_ENUMS(POW);
+COMPARE_ENUMS(PRELU);
+COMPARE_ENUMS(QUANTIZE);
+COMPARE_ENUMS(QUANTIZED_16BIT_LSTM);
+COMPARE_ENUMS(RANDOM_MULTINOMIAL);
+COMPARE_ENUMS(REDUCE_ALL);
+COMPARE_ENUMS(REDUCE_ANY);
+COMPARE_ENUMS(REDUCE_MAX);
+COMPARE_ENUMS(REDUCE_MIN);
+COMPARE_ENUMS(REDUCE_PROD);
+COMPARE_ENUMS(REDUCE_SUM);
+COMPARE_ENUMS(ROI_ALIGN);
+COMPARE_ENUMS(ROI_POOLING);
+COMPARE_ENUMS(RSQRT);
+COMPARE_ENUMS(SELECT);
+COMPARE_ENUMS(SIN);
+COMPARE_ENUMS(SLICE);
+COMPARE_ENUMS(SPLIT);
+COMPARE_ENUMS(SQRT);
+COMPARE_ENUMS(TILE);
+COMPARE_ENUMS(TOPK_V2);
+COMPARE_ENUMS(TRANSPOSE_CONV_2D);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(RESIZE_NEAREST_NEIGHBOR);
+COMPARE_ENUMS(QUANTIZED_LSTM);
+COMPARE_ENUMS(IF);
+COMPARE_ENUMS(WHILE);
+COMPARE_ENUMS(ELU);
+COMPARE_ENUMS(HARD_SWISH);
+COMPARE_ENUMS(FILL);
+COMPARE_ENUMS(RANK);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, Priority, Priority)
+
+COMPARE_ENUMS(LOW);
+COMPARE_ENUMS(MEDIUM);
+COMPARE_ENUMS(HIGH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperandLifeTime, Operand::LifeTime)
+
+COMPARE_ENUMS(TEMPORARY_VARIABLE);
+COMPARE_ENUMS(SUBGRAPH_INPUT);
+COMPARE_ENUMS(SUBGRAPH_OUTPUT);
+COMPARE_ENUMS(CONSTANT_COPY);
+COMPARE_ENUMS(CONSTANT_REFERENCE);
+COMPARE_ENUMS(NO_VALUE);
+COMPARE_ENUMS(SUBGRAPH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, ErrorStatus, ErrorStatus)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(DEVICE_UNAVAILABLE);
+COMPARE_ENUMS(GENERAL_FAILURE);
+COMPARE_ENUMS(OUTPUT_INSUFFICIENT_SIZE);
+COMPARE_ENUMS(INVALID_ARGUMENT);
+COMPARE_ENUMS(MISSED_DEADLINE_TRANSIENT);
+COMPARE_ENUMS(MISSED_DEADLINE_PERSISTENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_TRANSIENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_PERSISTENT);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+}  // anonymous namespace
diff --git a/neuralnetworks/1.3/utils/src/Conversions.cpp b/neuralnetworks/1.3/utils/src/Conversions.cpp
new file mode 100644
index 0000000..4c54e3b
--- /dev/null
+++ b/neuralnetworks/1.3/utils/src/Conversions.cpp
@@ -0,0 +1,552 @@
+/*
+ * 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/1.3/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.2/Conversions.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <algorithm>
+#include <chrono>
+#include <functional>
+#include <iterator>
+#include <limits>
+#include <type_traits>
+#include <utility>
+
+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 {
+
+constexpr auto validOperandType(nn::OperandType operandType) {
+    switch (operandType) {
+        case nn::OperandType::FLOAT32:
+        case nn::OperandType::INT32:
+        case nn::OperandType::UINT32:
+        case nn::OperandType::TENSOR_FLOAT32:
+        case nn::OperandType::TENSOR_INT32:
+        case nn::OperandType::TENSOR_QUANT8_ASYMM:
+        case nn::OperandType::BOOL:
+        case nn::OperandType::TENSOR_QUANT16_SYMM:
+        case nn::OperandType::TENSOR_FLOAT16:
+        case nn::OperandType::TENSOR_BOOL8:
+        case nn::OperandType::FLOAT16:
+        case nn::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+        case nn::OperandType::TENSOR_QUANT16_ASYMM:
+        case nn::OperandType::TENSOR_QUANT8_SYMM:
+        case nn::OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+        case nn::OperandType::SUBGRAPH:
+        case nn::OperandType::OEM:
+        case nn::OperandType::TENSOR_OEM_BYTE:
+            return true;
+    }
+    return nn::isExtension(operandType);
+}
+
+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>>> convertVec(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;
+}
+
+template <typename Type>
+Result<std::vector<ConvertOutput<Type>>> convert(const hidl_vec<Type>& arguments) {
+    return convertVec(arguments);
+}
+
+}  // anonymous namespace
+
+Result<OperandType> convert(const hal::V1_3::OperandType& operandType) {
+    return static_cast<OperandType>(operandType);
+}
+
+Result<OperationType> convert(const hal::V1_3::OperationType& operationType) {
+    return static_cast<OperationType>(operationType);
+}
+
+Result<Priority> convert(const hal::V1_3::Priority& priority) {
+    return static_cast<Priority>(priority);
+}
+
+Result<Capabilities> convert(const hal::V1_3::Capabilities& capabilities) {
+    const bool validOperandTypes = std::all_of(
+            capabilities.operandPerformance.begin(), capabilities.operandPerformance.end(),
+            [](const hal::V1_3::Capabilities::OperandPerformance& operandPerformance) {
+                const auto maybeType = convert(operandPerformance.type);
+                return !maybeType.has_value() ? false : validOperandType(maybeType.value());
+            });
+    if (!validOperandTypes) {
+        return NN_ERROR()
+               << "Invalid OperandType when converting OperandPerformance in Capabilities";
+    }
+
+    auto operandPerformance = NN_TRY(convert(capabilities.operandPerformance));
+    auto table =
+            NN_TRY(Capabilities::OperandPerformanceTable::create(std::move(operandPerformance)));
+
+    return Capabilities{
+            .relaxedFloat32toFloat16PerformanceScalar =
+                    NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
+            .relaxedFloat32toFloat16PerformanceTensor =
+                    NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
+            .operandPerformance = std::move(table),
+            .ifPerformance = NN_TRY(convert(capabilities.ifPerformance)),
+            .whilePerformance = NN_TRY(convert(capabilities.whilePerformance)),
+    };
+}
+
+Result<Capabilities::OperandPerformance> convert(
+        const hal::V1_3::Capabilities::OperandPerformance& operandPerformance) {
+    return Capabilities::OperandPerformance{
+            .type = NN_TRY(convert(operandPerformance.type)),
+            .info = NN_TRY(convert(operandPerformance.info)),
+    };
+}
+
+Result<Operation> convert(const hal::V1_3::Operation& operation) {
+    return Operation{
+            .type = NN_TRY(convert(operation.type)),
+            .inputs = operation.inputs,
+            .outputs = operation.outputs,
+    };
+}
+
+Result<Operand::LifeTime> convert(const hal::V1_3::OperandLifeTime& operandLifeTime) {
+    return static_cast<Operand::LifeTime>(operandLifeTime);
+}
+
+Result<Operand> convert(const hal::V1_3::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)),
+            .extraParams = NN_TRY(convert(operand.extraParams)),
+    };
+}
+
+Result<Model> convert(const hal::V1_3::Model& model) {
+    return Model{
+            .main = NN_TRY(convert(model.main)),
+            .referenced = NN_TRY(convert(model.referenced)),
+            .operandValues = NN_TRY(convert(model.operandValues)),
+            .pools = NN_TRY(convert(model.pools)),
+            .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+            .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
+    };
+}
+
+Result<Model::Subgraph> convert(const hal::V1_3::Subgraph& subgraph) {
+    auto operations = NN_TRY(convert(subgraph.operations));
+
+    // Verify number of consumers.
+    const auto numberOfConsumers =
+            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]) {
+            return NN_ERROR() << "Invalid numberOfConsumers for operand " << i << ", expected "
+                              << numberOfConsumers[i] << " but found "
+                              << subgraph.operands[i].numberOfConsumers;
+        }
+    }
+
+    return Model::Subgraph{
+            .operands = NN_TRY(convert(subgraph.operands)),
+            .operations = std::move(operations),
+            .inputIndexes = subgraph.inputIndexes,
+            .outputIndexes = subgraph.outputIndexes,
+    };
+}
+
+Result<BufferDesc> convert(const hal::V1_3::BufferDesc& bufferDesc) {
+    return BufferDesc{.dimensions = bufferDesc.dimensions};
+}
+
+Result<BufferRole> convert(const hal::V1_3::BufferRole& bufferRole) {
+    return BufferRole{
+            .modelIndex = bufferRole.modelIndex,
+            .ioIndex = bufferRole.ioIndex,
+            .frequency = bufferRole.frequency,
+    };
+}
+
+Result<Request> convert(const hal::V1_3::Request& request) {
+    return Request{
+            .inputs = NN_TRY(convert(request.inputs)),
+            .outputs = NN_TRY(convert(request.outputs)),
+            .pools = NN_TRY(convert(request.pools)),
+    };
+}
+
+Result<Request::MemoryPool> convert(const hal::V1_3::Request::MemoryPool& memoryPool) {
+    using Discriminator = hal::V1_3::Request::MemoryPool::hidl_discriminator;
+    switch (memoryPool.getDiscriminator()) {
+        case Discriminator::hidlMemory:
+            return createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
+        case Discriminator::token:
+            return static_cast<Request::MemoryDomainToken>(memoryPool.token());
+    }
+    return NN_ERROR() << "Invalid Request::MemoryPool discriminator "
+                      << underlyingType(memoryPool.getDiscriminator());
+}
+
+Result<OptionalTimePoint> convert(const hal::V1_3::OptionalTimePoint& optionalTimePoint) {
+    constexpr auto kTimePointMaxCount = TimePoint::max().time_since_epoch().count();
+    const auto makeTimePoint = [](uint64_t count) -> Result<OptionalTimePoint> {
+        if (count > kTimePointMaxCount) {
+            return NN_ERROR()
+                   << "Unable to convert OptionalTimePoint because the count exceeds the max";
+        }
+        const auto nanoseconds = std::chrono::nanoseconds{count};
+        return TimePoint{nanoseconds};
+    };
+
+    using Discriminator = hal::V1_3::OptionalTimePoint::hidl_discriminator;
+    switch (optionalTimePoint.getDiscriminator()) {
+        case Discriminator::none:
+            return std::nullopt;
+        case Discriminator::nanosecondsSinceEpoch:
+            return makeTimePoint(optionalTimePoint.nanosecondsSinceEpoch());
+    }
+    return NN_ERROR() << "Invalid OptionalTimePoint discriminator "
+                      << underlyingType(optionalTimePoint.getDiscriminator());
+}
+
+Result<OptionalTimeoutDuration> convert(
+        const hal::V1_3::OptionalTimeoutDuration& optionalTimeoutDuration) {
+    constexpr auto kTimeoutDurationMaxCount = TimeoutDuration::max().count();
+    const auto makeTimeoutDuration = [](uint64_t count) -> Result<OptionalTimeoutDuration> {
+        if (count > kTimeoutDurationMaxCount) {
+            return NN_ERROR()
+                   << "Unable to convert OptionalTimeoutDuration because the count exceeds the max";
+        }
+        return TimeoutDuration{count};
+    };
+
+    using Discriminator = hal::V1_3::OptionalTimeoutDuration::hidl_discriminator;
+    switch (optionalTimeoutDuration.getDiscriminator()) {
+        case Discriminator::none:
+            return std::nullopt;
+        case Discriminator::nanoseconds:
+            return makeTimeoutDuration(optionalTimeoutDuration.nanoseconds());
+    }
+    return NN_ERROR() << "Invalid OptionalTimeoutDuration discriminator "
+                      << underlyingType(optionalTimeoutDuration.getDiscriminator());
+}
+
+Result<ErrorStatus> convert(const hal::V1_3::ErrorStatus& status) {
+    switch (status) {
+        case hal::V1_3::ErrorStatus::NONE:
+        case hal::V1_3::ErrorStatus::DEVICE_UNAVAILABLE:
+        case hal::V1_3::ErrorStatus::GENERAL_FAILURE:
+        case hal::V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+        case hal::V1_3::ErrorStatus::INVALID_ARGUMENT:
+        case hal::V1_3::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+        case hal::V1_3::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+        case hal::V1_3::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+        case hal::V1_3::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+            return static_cast<ErrorStatus>(status);
+    }
+    return NN_ERROR() << "Invalid ErrorStatus " << underlyingType(status);
+}
+
+Result<std::vector<BufferRole>> convert(
+        const hardware::hidl_vec<hal::V1_3::BufferRole>& bufferRoles) {
+    return convertVec(bufferRoles);
+}
+
+}  // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+namespace {
+
+using utils::convert;
+
+nn::Result<V1_0::PerformanceInfo> convert(
+        const nn::Capabilities::PerformanceInfo& performanceInfo) {
+    return V1_0::utils::convert(performanceInfo);
+}
+
+nn::Result<V1_0::DataLocation> convert(const nn::DataLocation& dataLocation) {
+    return V1_0::utils::convert(dataLocation);
+}
+
+nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues) {
+    return V1_0::utils::convert(operandValues);
+}
+
+nn::Result<hidl_memory> convert(const nn::Memory& memory) {
+    return V1_0::utils::convert(memory);
+}
+
+nn::Result<V1_0::RequestArgument> convert(const nn::Request::Argument& argument) {
+    return V1_0::utils::convert(argument);
+}
+
+nn::Result<V1_2::Operand::ExtraParams> convert(const nn::Operand::ExtraParams& extraParams) {
+    return V1_2::utils::convert(extraParams);
+}
+
+nn::Result<V1_2::Model::ExtensionNameAndPrefix> convert(
+        const nn::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
+    return V1_2::utils::convert(extensionNameAndPrefix);
+}
+
+template <typename Input>
+using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;
+
+template <typename Type>
+nn::Result<hidl_vec<ConvertOutput<Type>>> convertVec(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(convert(arguments[i]));
+    }
+    return halObject;
+}
+
+template <typename Type>
+nn::Result<hidl_vec<ConvertOutput<Type>>> convert(const std::vector<Type>& arguments) {
+    return convertVec(arguments);
+}
+
+nn::Result<Request::MemoryPool> makeMemoryPool(const nn::Memory& memory) {
+    Request::MemoryPool ret;
+    ret.hidlMemory(NN_TRY(convert(memory)));
+    return ret;
+}
+
+nn::Result<Request::MemoryPool> makeMemoryPool(const nn::Request::MemoryDomainToken& token) {
+    Request::MemoryPool ret;
+    ret.token(underlyingType(token));
+    return ret;
+}
+
+nn::Result<Request::MemoryPool> makeMemoryPool(
+        const std::shared_ptr<const nn::IBuffer>& /*buffer*/) {
+    return NN_ERROR() << "Unable to make memory pool from IBuffer";
+}
+
+}  // 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<Priority> convert(const nn::Priority& priority) {
+    return static_cast<Priority>(priority);
+}
+
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
+    std::vector<nn::Capabilities::OperandPerformance> operandPerformance;
+    operandPerformance.reserve(capabilities.operandPerformance.asVector().size());
+    std::copy_if(capabilities.operandPerformance.asVector().begin(),
+                 capabilities.operandPerformance.asVector().end(),
+                 std::back_inserter(operandPerformance),
+                 [](const nn::Capabilities::OperandPerformance& operandPerformance) {
+                     return nn::validOperandType(operandPerformance.type);
+                 });
+
+    return Capabilities{
+            .relaxedFloat32toFloat16PerformanceScalar =
+                    NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
+            .relaxedFloat32toFloat16PerformanceTensor =
+                    NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
+            .operandPerformance = NN_TRY(convert(operandPerformance)),
+            .ifPerformance = NN_TRY(convert(capabilities.ifPerformance)),
+            .whilePerformance = NN_TRY(convert(capabilities.whilePerformance)),
+    };
+}
+
+nn::Result<Capabilities::OperandPerformance> convert(
+        const nn::Capabilities::OperandPerformance& operandPerformance) {
+    return Capabilities::OperandPerformance{
+            .type = NN_TRY(convert(operandPerformance.type)),
+            .info = NN_TRY(convert(operandPerformance.info)),
+    };
+}
+
+nn::Result<Operation> convert(const nn::Operation& operation) {
+    return Operation{
+            .type = NN_TRY(convert(operation.type)),
+            .inputs = operation.inputs,
+            .outputs = operation.outputs,
+    };
+}
+
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& operandLifeTime) {
+    if (operandLifeTime == nn::Operand::LifeTime::POINTER) {
+        return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+    }
+    return static_cast<OperandLifeTime>(operandLifeTime);
+}
+
+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)),
+            .extraParams = NN_TRY(convert(operand.extraParams)),
+    };
+}
+
+nn::Result<Model> convert(const nn::Model& model) {
+    if (!hal::utils::hasNoPointerData(model)) {
+        return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+    }
+
+    return Model{
+            .main = NN_TRY(convert(model.main)),
+            .referenced = NN_TRY(convert(model.referenced)),
+            .operandValues = NN_TRY(convert(model.operandValues)),
+            .pools = NN_TRY(convert(model.pools)),
+            .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+            .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
+    };
+}
+
+nn::Result<Subgraph> convert(const nn::Model::Subgraph& subgraph) {
+    auto operands = NN_TRY(convert(subgraph.operands));
+
+    // Update number of consumers.
+    const auto numberOfConsumers =
+            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];
+    }
+
+    return Subgraph{
+            .operands = std::move(operands),
+            .operations = NN_TRY(convert(subgraph.operations)),
+            .inputIndexes = subgraph.inputIndexes,
+            .outputIndexes = subgraph.outputIndexes,
+    };
+}
+
+nn::Result<BufferDesc> convert(const nn::BufferDesc& bufferDesc) {
+    return BufferDesc{.dimensions = bufferDesc.dimensions};
+}
+
+nn::Result<BufferRole> convert(const nn::BufferRole& bufferRole) {
+    return BufferRole{
+            .modelIndex = bufferRole.modelIndex,
+            .ioIndex = bufferRole.ioIndex,
+            .frequency = bufferRole.frequency,
+    };
+}
+
+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<Request::MemoryPool> convert(const nn::Request::MemoryPool& memoryPool) {
+    return std::visit([](const auto& o) { return makeMemoryPool(o); }, memoryPool);
+}
+
+nn::Result<OptionalTimePoint> convert(const nn::OptionalTimePoint& optionalTimePoint) {
+    OptionalTimePoint ret;
+    if (optionalTimePoint.has_value()) {
+        const auto count = optionalTimePoint.value().time_since_epoch().count();
+        if (count < 0) {
+            return NN_ERROR() << "Unable to convert OptionalTimePoint because time since epoch "
+                                 "count is negative";
+        }
+        ret.nanosecondsSinceEpoch(count);
+    }
+    return ret;
+}
+
+nn::Result<OptionalTimeoutDuration> convert(
+        const nn::OptionalTimeoutDuration& optionalTimeoutDuration) {
+    OptionalTimeoutDuration ret;
+    if (optionalTimeoutDuration.has_value()) {
+        const auto count = optionalTimeoutDuration.value().count();
+        if (count < 0) {
+            return NN_ERROR()
+                   << "Unable to convert OptionalTimeoutDuration because count is negative";
+        }
+        ret.nanoseconds(count);
+    }
+    return ret;
+}
+
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& errorStatus) {
+    switch (errorStatus) {
+        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:
+        case nn::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+        case nn::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+        case nn::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+        case nn::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+            return static_cast<ErrorStatus>(errorStatus);
+        default:
+            return ErrorStatus::GENERAL_FAILURE;
+    }
+}
+
+nn::Result<hidl_vec<BufferRole>> convert(const std::vector<nn::BufferRole>& bufferRoles) {
+    return convertVec(bufferRoles);
+}
+
+}  // namespace android::hardware::neuralnetworks::V1_3::utils