diff --git a/neuralnetworks/1.3/vts/functional/Android.bp b/neuralnetworks/1.3/vts/functional/Android.bp
index f936267..545a5be 100644
--- a/neuralnetworks/1.3/vts/functional/Android.bp
+++ b/neuralnetworks/1.3/vts/functional/Android.bp
@@ -40,6 +40,7 @@
         "BasicTests.cpp",
         "CompilationCachingTests.cpp",
         "GeneratedTestHarness.cpp",
+        "MemoryDomainTests.cpp",
         "QualityOfServiceTests.cpp",
         "TestAssertions.cpp",
         "ValidateBurst.cpp",
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
index 5689a39..ff21960 100644
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -72,8 +72,6 @@
 
 namespace {
 
-enum class Executor { ASYNC, SYNC, BURST, FENCED };
-
 enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT, MISSED_DEADLINE };
 
 enum class MemoryType { SHARED, DEVICE };
diff --git a/neuralnetworks/1.3/vts/functional/MemoryDomainTests.cpp b/neuralnetworks/1.3/vts/functional/MemoryDomainTests.cpp
new file mode 100644
index 0000000..08c1b35
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/MemoryDomainTests.cpp
@@ -0,0 +1,1166 @@
+/*
+ * 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.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include <android-base/logging.h>
+#include <gtest/gtest.h>
+
+#include "1.3/Callbacks.h"
+#include "1.3/Utils.h"
+#include "GeneratedTestHarness.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android::hardware::neuralnetworks::V1_3::vts::functional {
+
+using namespace test_helper;
+using implementation::ExecutionCallback;
+using implementation::PreparedModelCallback;
+using V1_0::RequestArgument;
+using V1_1::ExecutionPreference;
+using V1_2::Constant;
+using V1_2::MeasureTiming;
+using V1_2::OutputShape;
+using V1_2::Timing;
+
+namespace {
+
+const auto kNamedDeviceChoices = testing::ValuesIn(getNamedDevices());
+
+// A 1.3 driver is likely to support at least one of the following operand types.
+const std::vector<TestOperandType> kTestOperandTypeChoicesVector = {
+        TestOperandType::TENSOR_FLOAT32,
+        TestOperandType::TENSOR_FLOAT16,
+        TestOperandType::TENSOR_QUANT8_ASYMM,
+        TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+};
+const auto kTestOperandTypeChoices = testing::ValuesIn(kTestOperandTypeChoicesVector);
+
+bool isInChoices(TestOperandType type) {
+    return std::count(kTestOperandTypeChoicesVector.begin(), kTestOperandTypeChoicesVector.end(),
+                      type) > 0;
+}
+
+bool isFloat(TestOperandType type) {
+    CHECK(isInChoices(type));
+    return type == TestOperandType::TENSOR_FLOAT32 || type == TestOperandType::TENSOR_FLOAT16;
+}
+
+// Create dummy buffers for model constants as well as inputs and outputs.
+// We only care about the size here because we will not check accuracy in validation tests.
+void createDummyData(TestModel* testModel) {
+    for (auto& operand : testModel->main.operands) {
+        if (operand.data != nullptr) continue;
+        switch (operand.lifetime) {
+            case TestOperandLifeTime::SUBGRAPH_INPUT:
+            case TestOperandLifeTime::SUBGRAPH_OUTPUT:
+            case TestOperandLifeTime::CONSTANT_COPY:
+            case TestOperandLifeTime::CONSTANT_REFERENCE: {
+                const uint32_t size = nn::nonExtensionOperandSizeOfData(
+                        static_cast<OperandType>(operand.type), operand.dimensions);
+                operand.data = TestBuffer(size);
+            } break;
+            default:
+                break;
+        }
+    }
+}
+
+TestOperand createInt32Scalar(int32_t value) {
+    return {
+            .type = TestOperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = TestOperandLifeTime::CONSTANT_COPY,
+            .data = TestBuffer::createFromVector<int32_t>({value}),
+    };
+}
+
+// Construct a test model with multiple CONV_2D operations with the given operand as inputs.
+// The dimensions of the filters are chosen to ensure outputs has the same dimensions as inputs.
+// We choose CONV_2D operation because it is commonly supported by most drivers.
+TestModel createConvModel(const TestOperand& operand, uint32_t numOperations) {
+    CHECK(isInChoices(operand.type));
+
+    TestOperand weight = {.type = operand.type,
+                          .dimensions = {operand.dimensions[3], 3, 3, operand.dimensions[3]},
+                          .numberOfConsumers = 1,
+                          .scale = isFloat(operand.type) ? 0.0f : 1.0f,
+                          .zeroPoint = 0,
+                          .lifetime = TestOperandLifeTime::CONSTANT_COPY};
+
+    TestOperand bias = {
+            .type = isFloat(operand.type) ? operand.type : TestOperandType::TENSOR_INT32,
+            .dimensions = {operand.dimensions[3]},
+            .numberOfConsumers = 1,
+            .scale = operand.scale * weight.scale,
+            .zeroPoint = 0,
+            .lifetime = TestOperandLifeTime::CONSTANT_COPY};
+
+    TestOperand output = operand;
+    output.numberOfConsumers = 0;
+    output.lifetime = TestOperandLifeTime::SUBGRAPH_OUTPUT;
+
+    const std::vector<TestOperand> operands = {
+            operand,
+            std::move(weight),
+            std::move(bias),
+            createInt32Scalar(1),  // same padding
+            createInt32Scalar(1),  // width stride
+            createInt32Scalar(1),  // height stride
+            createInt32Scalar(0),  // activation = NONE
+            std::move(output),
+    };
+
+    TestModel model;
+    for (uint32_t i = 0; i < numOperations; i++) {
+        model.main.operands.insert(model.main.operands.end(), operands.begin(), operands.end());
+        const uint32_t inputIndex = operands.size() * i;
+        const uint32_t outputIndex = inputIndex + operands.size() - 1;
+        std::vector<uint32_t> inputs(operands.size() - 1);
+        std::iota(inputs.begin(), inputs.end(), inputIndex);
+        model.main.operations.push_back({.type = TestOperationType::CONV_2D,
+                                         .inputs = std::move(inputs),
+                                         .outputs = {outputIndex}});
+        model.main.inputIndexes.push_back(inputIndex);
+        model.main.outputIndexes.push_back(outputIndex);
+    }
+    createDummyData(&model);
+    return model;
+}
+
+// Construct a test model with a single ADD operation with the given operand as input0 and input1.
+// This is to cover additional cases that the CONV_2D model does not support, e.g. arbitrary input
+// operand rank, scalar input operand. We choose ADD operation because it is commonly supported by
+// most drivers.
+TestModel createSingleAddModel(const TestOperand& operand) {
+    CHECK(isInChoices(operand.type));
+
+    TestOperand act = {
+            .type = TestOperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+    };
+
+    TestOperand output = operand;
+    output.numberOfConsumers = 0;
+    output.lifetime = TestOperandLifeTime::SUBGRAPH_OUTPUT;
+
+    TestModel model = {
+            .main =
+                    {
+                            .operands =
+                                    {
+                                            operand,
+                                            operand,
+                                            std::move(act),
+                                            output,
+                                    },
+                            .operations = {{.type = TestOperationType::ADD,
+                                            .inputs = {0, 1, 2},
+                                            .outputs = {3}}},
+                            .inputIndexes = {0, 1, 2},
+                            .outputIndexes = {3},
+                    },
+    };
+    createDummyData(&model);
+    return model;
+}
+
+// A dummy invalid IPreparedModel class for MemoryDomainAllocateTest.InvalidPreparedModel
+class InvalidPreparedModel : public IPreparedModel {
+  public:
+    Return<V1_0::ErrorStatus> execute(const V1_0::Request&,
+                                      const sp<V1_0::IExecutionCallback>&) override {
+        return V1_0::ErrorStatus::GENERAL_FAILURE;
+    }
+    Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request&, V1_2::MeasureTiming,
+                                          const sp<V1_2::IExecutionCallback>&) override {
+        return V1_0::ErrorStatus::GENERAL_FAILURE;
+    }
+    Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request&, V1_2::MeasureTiming,
+                                          const V1_3::OptionalTimePoint&,
+                                          const V1_3::OptionalTimeoutDuration&,
+                                          const sp<V1_3::IExecutionCallback>&) override {
+        return V1_3::ErrorStatus::GENERAL_FAILURE;
+    }
+    Return<void> executeSynchronously(const V1_0::Request&, V1_2::MeasureTiming,
+                                      executeSynchronously_cb) override {
+        return Void();
+    }
+    Return<void> executeSynchronously_1_3(const V1_3::Request&, V1_2::MeasureTiming,
+                                          const V1_3::OptionalTimePoint&,
+                                          const V1_3::OptionalTimeoutDuration&,
+                                          executeSynchronously_1_3_cb) override {
+        return Void();
+    }
+    Return<void> configureExecutionBurst(const sp<V1_2::IBurstCallback>&,
+                                         const MQDescriptorSync<V1_2::FmqRequestDatum>&,
+                                         const MQDescriptorSync<V1_2::FmqResultDatum>&,
+                                         configureExecutionBurst_cb) override {
+        return Void();
+    }
+    Return<void> executeFenced(const V1_3::Request&, const hidl_vec<hidl_handle>&,
+                               V1_2::MeasureTiming, const V1_3::OptionalTimePoint&,
+                               const V1_3::OptionalTimeoutDuration&,
+                               const V1_3::OptionalTimeoutDuration&, executeFenced_cb) override {
+        return Void();
+    }
+};
+
+}  // namespace
+
+class MemoryDomainTestBase : public testing::Test {
+  protected:
+    MemoryDomainTestBase(sp<IDevice> device, TestOperandType type)
+        : kDevice(std::move(device)),
+          kTestOperandType(type),
+          kTestOperand(kTestOperandMap.at(type)),
+          kTestOperandDataSize(nn::nonExtensionOperandSizeOfData(static_cast<OperandType>(type),
+                                                                 kTestOperand.dimensions)) {}
+
+    void SetUp() override {
+        testing::Test::SetUp();
+        ASSERT_NE(kDevice, nullptr);
+    }
+
+    sp<IPreparedModel> createConvPreparedModel(const TestOperand& testOperand,
+                                               uint32_t numOperations = 1) {
+        const TestModel testModel = createConvModel(testOperand, numOperations);
+        const Model model = createModel(testModel);
+        sp<IPreparedModel> preparedModel;
+        createPreparedModel(kDevice, model, &preparedModel, /*reportSkipping=*/false);
+        return preparedModel;
+    }
+
+    sp<IPreparedModel> createAddPreparedModel(const TestOperand& testOperand) {
+        const TestModel testModel = createSingleAddModel(testOperand);
+        const Model model = createModel(testModel);
+        sp<IPreparedModel> preparedModel;
+        createPreparedModel(kDevice, model, &preparedModel, /*reportSkipping=*/false);
+        return preparedModel;
+    }
+
+    static const std::map<TestOperandType, TestOperand> kTestOperandMap;
+
+    const sp<IDevice> kDevice;
+    const TestOperandType kTestOperandType;
+    const TestOperand& kTestOperand;
+    const uint32_t kTestOperandDataSize;
+};
+
+const std::map<TestOperandType, TestOperand> MemoryDomainTestBase::kTestOperandMap = {
+        {TestOperandType::TENSOR_FLOAT32,
+         {
+                 .type = TestOperandType::TENSOR_FLOAT32,
+                 .dimensions = {1, 32, 32, 8},
+                 .numberOfConsumers = 1,
+                 .scale = 0.0f,
+                 .zeroPoint = 0,
+                 .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+         }},
+        {TestOperandType::TENSOR_FLOAT16,
+         {
+                 .type = TestOperandType::TENSOR_FLOAT16,
+                 .dimensions = {1, 32, 32, 8},
+                 .numberOfConsumers = 1,
+                 .scale = 0.0f,
+                 .zeroPoint = 0,
+                 .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+         }},
+        {TestOperandType::TENSOR_QUANT8_ASYMM,
+         {
+                 .type = TestOperandType::TENSOR_QUANT8_ASYMM,
+                 .dimensions = {1, 32, 32, 8},
+                 .numberOfConsumers = 1,
+                 .scale = 0.5f,
+                 .zeroPoint = 0,
+                 .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+         }},
+        {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+         {
+                 .type = TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED,
+                 .dimensions = {1, 32, 32, 8},
+                 .numberOfConsumers = 1,
+                 .scale = 0.5f,
+                 .zeroPoint = 0,
+                 .lifetime = TestOperandLifeTime::SUBGRAPH_INPUT,
+         }},
+};
+
+using MemoryDomainAllocateTestParam = std::tuple<NamedDevice, TestOperandType>;
+class MemoryDomainAllocateTest : public MemoryDomainTestBase,
+                                 public testing::WithParamInterface<MemoryDomainAllocateTestParam> {
+  protected:
+    MemoryDomainAllocateTest()
+        : MemoryDomainTestBase(getData(std::get<NamedDevice>(GetParam())),
+                               std::get<TestOperandType>(GetParam())) {}
+
+    struct AllocateTestArgs {
+        hidl_vec<uint32_t> dimensions;
+        hidl_vec<sp<IPreparedModel>> preparedModels;
+        hidl_vec<BufferRole> inputRoles;
+        hidl_vec<BufferRole> outputRoles;
+    };
+
+    // Validation test for IDevice::allocate. The driver is expected to fail with INVALID_ARGUMENT,
+    // or GENERAL_FAILURE if memory domain is not supported.
+    void validateAllocate(AllocateTestArgs args) {
+        const auto ret = kDevice->allocate(
+                {.dimensions = std::move(args.dimensions)}, std::move(args.preparedModels),
+                std::move(args.inputRoles), std::move(args.outputRoles),
+                [](ErrorStatus status, const sp<IBuffer>& buffer, uint32_t token) {
+                    EXPECT_TRUE(status == ErrorStatus::INVALID_ARGUMENT ||
+                                status == ErrorStatus::GENERAL_FAILURE);
+                    EXPECT_EQ(buffer, nullptr);
+                    EXPECT_EQ(token, 0);
+                });
+        ASSERT_TRUE(ret.isOk());
+    }
+
+    void testConflictOperands(const sp<IPreparedModel>& model1, const sp<IPreparedModel>& model2) {
+        validateAllocate({
+                .preparedModels = {model1, model2},
+                .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                               {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+        });
+        validateAllocate({
+                .preparedModels = {model1, model2},
+                .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+                .outputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+        });
+        validateAllocate({
+                .preparedModels = {model1, model2},
+                .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                                {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+        });
+    }
+};
+
+TEST_P(MemoryDomainAllocateTest, EmptyRole) {
+    // Test with empty prepared models and roles.
+    validateAllocate({});
+
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    // Test again with non-empty prepared models but empty roles.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, NullptrPreparedModel) {
+    // Test with nullptr prepared model as input role.
+    validateAllocate({
+            .preparedModels = {nullptr},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+
+    // Test with nullptr prepared model as output role.
+    validateAllocate({
+            .preparedModels = {nullptr},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidPreparedModel) {
+    sp<InvalidPreparedModel> invalidPreparedModel = new InvalidPreparedModel();
+
+    // Test with invalid prepared model as input role.
+    validateAllocate({
+            .preparedModels = {invalidPreparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+
+    // Test with invalid prepared model as output role.
+    validateAllocate({
+            .preparedModels = {invalidPreparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidModelIndex) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    // This should fail, because the model index is out of bound.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+    });
+
+    // This should fail, because the model index is out of bound.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .outputRoles = {{.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidIOIndex) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    // This should fail, because the model only has one input.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 1, .frequency = 1.0f}},
+    });
+
+    // This should fail, because the model only has one output.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 1, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, InvalidFrequency) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    for (float invalidFreq : {10.0f, 0.0f, -0.5f}) {
+        // Test with invalid frequency for input roles.
+        validateAllocate({
+                .preparedModels = {preparedModel},
+                .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = invalidFreq}},
+        });
+        // Test with invalid frequency for output roles.
+        validateAllocate({
+                .preparedModels = {preparedModel},
+                .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = invalidFreq}},
+        });
+    }
+}
+
+TEST_P(MemoryDomainAllocateTest, SameRoleSpecifiedTwice) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    // Same role with same model index.
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                           {.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+    validateAllocate({
+            .preparedModels = {preparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                            {.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+
+    // Different model indexes, but logically referring to the same role.
+    validateAllocate({
+            .preparedModels = {preparedModel, preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                           {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+    });
+    validateAllocate({
+            .preparedModels = {preparedModel, preparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f},
+                            {.modelIndex = 1, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictOperandType) {
+    const std::map<TestOperandType, TestOperandType> conflictTypeMap = {
+            {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16},
+            {TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_FLOAT32},
+            {TestOperandType::TENSOR_QUANT8_ASYMM, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+            {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED, TestOperandType::TENSOR_QUANT8_ASYMM},
+    };
+
+    TestOperand conflictTestOperand = kTestOperand;
+    const auto it = conflictTypeMap.find(kTestOperandType);
+    ASSERT_FALSE(it == conflictTypeMap.end());
+    conflictTestOperand.type = it->second;
+
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+    if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+    testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictScale) {
+    if (isFloat(kTestOperandType)) return;
+
+    TestOperand conflictTestOperand = kTestOperand;
+    ASSERT_NE(conflictTestOperand.scale, 1.0f);
+    conflictTestOperand.scale = 1.0f;
+
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+    if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+    testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictZeroPoint) {
+    if (isFloat(kTestOperandType)) return;
+
+    TestOperand conflictTestOperand = kTestOperand;
+    ASSERT_NE(conflictTestOperand.zeroPoint, 10);
+    conflictTestOperand.zeroPoint = 10;
+
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+    if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+    testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankBetweenRoles) {
+    TestOperand conflictTestOperand = kTestOperand;
+    conflictTestOperand.dimensions.pop_back();
+
+    auto preparedModel = createAddPreparedModel(kTestOperand);
+    auto conflictPreparedModel = createAddPreparedModel(conflictTestOperand);
+    if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+    testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictDimensionsBetweenRoles) {
+    TestOperand conflictTestOperand = kTestOperand;
+    conflictTestOperand.dimensions[0] = 4;
+
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto conflictPreparedModel = createConvPreparedModel(conflictTestOperand);
+    if (preparedModel == nullptr || conflictPreparedModel == nullptr) return;
+    testConflictOperands(preparedModel, conflictPreparedModel);
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankBetweenRoleAndDesc) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    auto badDimensions = kTestOperand.dimensions;
+    badDimensions.pop_back();
+
+    validateAllocate({
+            .dimensions = badDimensions,
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+    validateAllocate({
+            .dimensions = badDimensions,
+            .preparedModels = {preparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictDimensionsBetweenRoleAndDesc) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    auto badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 4;
+
+    validateAllocate({
+            .dimensions = badDimensions,
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+    validateAllocate({
+            .dimensions = badDimensions,
+            .preparedModels = {preparedModel},
+            .outputRoles = {{.modelIndex = 0, .ioIndex = 0, .frequency = 1.0f}},
+    });
+}
+
+TEST_P(MemoryDomainAllocateTest, ConflictRankWithScalarRole) {
+    auto preparedModel = createAddPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    // This should fail, because the target operand is a scalar but a non-empty dimension is
+    // specified.
+    validateAllocate({
+            .dimensions = {1},
+            .preparedModels = {preparedModel},
+            .inputRoles = {{.modelIndex = 0, .ioIndex = 2, .frequency = 1.0f}},
+    });
+}
+
+std::string printMemoryDomainAllocateTest(
+        const testing::TestParamInfo<MemoryDomainAllocateTestParam>& info) {
+    const auto& [namedDevice, operandType] = info.param;
+    const std::string type = toString(static_cast<OperandType>(operandType));
+    return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+INSTANTIATE_TEST_CASE_P(TestMemoryDomain, MemoryDomainAllocateTest,
+                        testing::Combine(kNamedDeviceChoices, kTestOperandTypeChoices),
+                        printMemoryDomainAllocateTest);
+
+class MemoryDomainCopyTestBase : public MemoryDomainTestBase {
+  protected:
+    MemoryDomainCopyTestBase(sp<IDevice> device, TestOperandType type)
+        : MemoryDomainTestBase(std::move(device), type) {}
+
+    // Allocates device memory for roles of a single prepared model.
+    // Returns {IBuffer, token} if success; returns {nullptr, 0} if not supported.
+    std::pair<sp<IBuffer>, uint32_t> allocateBuffer(const sp<IPreparedModel>& preparedModel,
+                                                    const std::vector<uint32_t>& inputIndexes,
+                                                    const std::vector<uint32_t>& outputIndexes,
+                                                    const std::vector<uint32_t>& dimensions) {
+        if (preparedModel == nullptr) {
+            return {nullptr, 0};
+        }
+
+        hidl_vec<BufferRole> inputRoles(inputIndexes.size()), outputRoles(outputIndexes.size());
+        auto trans = [](uint32_t ind) -> BufferRole {
+            return {.modelIndex = 0, .ioIndex = ind, .frequency = 1.0f};
+        };
+        std::transform(inputIndexes.begin(), inputIndexes.end(), inputRoles.begin(), trans);
+        std::transform(outputIndexes.begin(), outputIndexes.end(), outputRoles.begin(), trans);
+
+        sp<IBuffer> buffer;
+        uint32_t token = 0;
+        const auto ret = kDevice->allocate(
+                {.dimensions = dimensions}, {preparedModel}, std::move(inputRoles),
+                std::move(outputRoles),
+                [&buffer, &token](ErrorStatus err, const sp<IBuffer>& buf, uint32_t tok) {
+                    if (err == ErrorStatus::NONE) {
+                        EXPECT_NE(buf, nullptr);
+                        EXPECT_GT(tok, 0);
+                        buffer = buf;
+                        token = tok;
+                    } else {
+                        EXPECT_EQ(err, ErrorStatus::GENERAL_FAILURE);
+                        EXPECT_EQ(buf, nullptr);
+                        EXPECT_EQ(tok, 0);
+                    }
+                });
+        EXPECT_TRUE(ret.isOk());
+        return {std::move(buffer), token};
+    }
+
+    std::pair<sp<IBuffer>, uint32_t> allocateBuffer(const sp<IPreparedModel>& preparedModel,
+                                                    const std::vector<uint32_t>& inputIndexes,
+                                                    const std::vector<uint32_t>& outputIndexes) {
+        return allocateBuffer(preparedModel, inputIndexes, outputIndexes, {});
+    }
+
+    hidl_memory allocateSharedMemory(uint32_t size) {
+        hidl_memory memory = nn::allocateSharedMemory(size);
+        EXPECT_EQ(memory.size(), size);
+        return memory;
+    }
+
+    void testCopyFrom(const sp<IBuffer>& buffer, const hidl_memory& memory,
+                      const std::vector<uint32_t>& dimensions, ErrorStatus expectedStatus) {
+        const auto ret = buffer->copyFrom(memory, dimensions);
+        ASSERT_TRUE(ret.isOk());
+        ASSERT_EQ(static_cast<ErrorStatus>(ret), expectedStatus);
+    }
+
+    void testCopyTo(const sp<IBuffer>& buffer, const hidl_memory& memory,
+                    ErrorStatus expectedStatus) {
+        const auto ret = buffer->copyTo(memory);
+        ASSERT_TRUE(ret.isOk());
+        ASSERT_EQ(static_cast<ErrorStatus>(ret), expectedStatus);
+    }
+
+    void initializeDeviceMemory(const sp<IBuffer>& buffer) {
+        hidl_memory memory = nn::allocateSharedMemory(kTestOperandDataSize);
+        ASSERT_EQ(memory.size(), kTestOperandDataSize);
+        testCopyFrom(buffer, memory, kTestOperand.dimensions, ErrorStatus::NONE);
+    }
+};
+
+using MemoryDomainCopyTestParam = std::tuple<NamedDevice, TestOperandType>;
+class MemoryDomainCopyTest : public MemoryDomainCopyTestBase,
+                             public testing::WithParamInterface<MemoryDomainCopyTestParam> {
+  protected:
+    MemoryDomainCopyTest()
+        : MemoryDomainCopyTestBase(getData(std::get<NamedDevice>(GetParam())),
+                                   std::get<TestOperandType>(GetParam())) {}
+};
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidMemorySize) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+    hidl_memory badMemory1 = allocateSharedMemory(badMemorySize1);
+    hidl_memory badMemory2 = allocateSharedMemory(badMemorySize2);
+    testCopyFrom(buffer, badMemory1, {}, ErrorStatus::INVALID_ARGUMENT);
+    testCopyFrom(buffer, badMemory2, {}, ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidMemorySize_DynamicShape) {
+    TestOperand testOperand = kTestOperand;
+    testOperand.dimensions[0] = 0;
+    auto preparedModel = createConvPreparedModel(testOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+    hidl_memory badMemory1 = allocateSharedMemory(badMemorySize1);
+    hidl_memory badMemory2 = allocateSharedMemory(badMemorySize2);
+    hidl_memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+    auto badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 2;
+
+    testCopyFrom(buffer, badMemory1, kTestOperand.dimensions, ErrorStatus::INVALID_ARGUMENT);
+    testCopyFrom(buffer, badMemory2, kTestOperand.dimensions, ErrorStatus::INVALID_ARGUMENT);
+    testCopyFrom(buffer, goodMemory, kTestOperand.dimensions, ErrorStatus::NONE);
+    testCopyFrom(buffer, goodMemory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidDimensions) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    hidl_memory memory = allocateSharedMemory(kTestOperandDataSize);
+
+    std::vector<uint32_t> badDimensions;
+    badDimensions = kTestOperand.dimensions;
+    badDimensions.pop_back();
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 2;
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 0;
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    testCopyFrom(buffer, memory, {}, ErrorStatus::NONE);
+    testCopyFrom(buffer, memory, kTestOperand.dimensions, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyFrom_InvalidDimensions_DynamicShape) {
+    TestOperand testOperand = kTestOperand;
+    testOperand.dimensions[0] = 0;
+    auto preparedModel = createConvPreparedModel(testOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    hidl_memory memory = allocateSharedMemory(kTestOperandDataSize);
+
+    std::vector<uint32_t> badDimensions;
+    badDimensions = kTestOperand.dimensions;
+    badDimensions.pop_back();
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 2;
+    badDimensions[3] = 4;
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 1;
+    badDimensions[3] = 0;
+    testCopyFrom(buffer, memory, badDimensions, ErrorStatus::INVALID_ARGUMENT);
+
+    testCopyFrom(buffer, memory, {}, ErrorStatus::INVALID_ARGUMENT);
+    testCopyFrom(buffer, memory, kTestOperand.dimensions, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_UninitializedMemory) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    hidl_memory memory = allocateSharedMemory(kTestOperandDataSize);
+    testCopyTo(buffer, memory, ErrorStatus::GENERAL_FAILURE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_InvalidMemorySize) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+    hidl_memory badMemory1 = allocateSharedMemory(badMemorySize1);
+    hidl_memory badMemory2 = allocateSharedMemory(badMemorySize2);
+    hidl_memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+    initializeDeviceMemory(buffer);
+    testCopyTo(buffer, badMemory1, ErrorStatus::INVALID_ARGUMENT);
+    testCopyTo(buffer, badMemory2, ErrorStatus::INVALID_ARGUMENT);
+    testCopyTo(buffer, goodMemory, ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainCopyTest, CopyTo_InvalidMemorySize_DynamicShape) {
+    TestOperand testOperand = kTestOperand;
+    testOperand.dimensions[0] = 0;
+    auto preparedModel = createConvPreparedModel(testOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    uint32_t badMemorySize1 = kTestOperandDataSize / 2, badMemorySize2 = kTestOperandDataSize * 2;
+    hidl_memory badMemory1 = allocateSharedMemory(badMemorySize1);
+    hidl_memory badMemory2 = allocateSharedMemory(badMemorySize2);
+    hidl_memory goodMemory = allocateSharedMemory(kTestOperandDataSize);
+
+    initializeDeviceMemory(buffer);
+    testCopyTo(buffer, badMemory1, ErrorStatus::INVALID_ARGUMENT);
+    testCopyTo(buffer, badMemory2, ErrorStatus::INVALID_ARGUMENT);
+    testCopyTo(buffer, goodMemory, ErrorStatus::NONE);
+}
+
+std::string printMemoryDomainCopyTest(
+        const testing::TestParamInfo<MemoryDomainCopyTestParam>& info) {
+    const auto& [namedDevice, operandType] = info.param;
+    const std::string type = toString(static_cast<OperandType>(operandType));
+    return gtestCompliantName(getName(namedDevice) + "_" + type);
+}
+
+INSTANTIATE_TEST_CASE_P(TestMemoryDomain, MemoryDomainCopyTest,
+                        testing::Combine(kNamedDeviceChoices, kTestOperandTypeChoices),
+                        printMemoryDomainCopyTest);
+
+using MemoryDomainExecutionTestParam = std::tuple<NamedDevice, TestOperandType, Executor>;
+class MemoryDomainExecutionTest
+    : public MemoryDomainCopyTestBase,
+      public testing::WithParamInterface<MemoryDomainExecutionTestParam> {
+  protected:
+    MemoryDomainExecutionTest()
+        : MemoryDomainCopyTestBase(getData(std::get<NamedDevice>(GetParam())),
+                                   std::get<TestOperandType>(GetParam())) {}
+
+    Request::MemoryPool createSharedMemoryPool(uint32_t size) {
+        hidl_memory memory = allocateSharedMemory(size);
+        Request::MemoryPool pool;
+        pool.hidlMemory(memory);
+        return pool;
+    }
+
+    Request::MemoryPool createDeviceMemoryPool(uint32_t token) {
+        Request::MemoryPool pool;
+        pool.token(token);
+        return pool;
+    }
+
+    void testExecution(const sp<IPreparedModel>& preparedModel, const Request& request,
+                       ErrorStatus expectedStatus) {
+        switch (kExecutor) {
+            case Executor::ASYNC:
+                EXPECT_EQ(executeAsync(preparedModel, request), expectedStatus);
+                break;
+            case Executor::SYNC:
+                EXPECT_EQ(executeSync(preparedModel, request), expectedStatus);
+                break;
+            default:
+                ASSERT_TRUE(false);
+        }
+    }
+
+    ErrorStatus executeAsync(const sp<IPreparedModel>& preparedModel, const Request& request) {
+        ErrorStatus executionStatus;
+
+        // launch execution
+        sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+        const auto ret =
+                preparedModel->execute_1_3(request, MeasureTiming::NO, {}, {}, executionCallback);
+        EXPECT_TRUE(ret.isOk());
+        executionStatus = static_cast<ErrorStatus>(ret);
+
+        // retrieve execution status
+        executionCallback->wait();
+        if (executionStatus == ErrorStatus::NONE) {
+            executionStatus = executionCallback->getStatus();
+        } else {
+            EXPECT_EQ(executionStatus, executionCallback->getStatus());
+        }
+        const auto timing = executionCallback->getTiming();
+        EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
+        EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
+        if (executionStatus != ErrorStatus::NONE) {
+            EXPECT_EQ(executionCallback->getOutputShapes().size(), 0);
+        }
+        return executionStatus;
+    }
+
+    ErrorStatus executeSync(const sp<IPreparedModel>& preparedModel, const Request& request) {
+        ErrorStatus executionStatus;
+        const auto ret = preparedModel->executeSynchronously_1_3(
+                request, MeasureTiming::NO, {}, {},
+                [&executionStatus](ErrorStatus error, const hidl_vec<OutputShape>& shapes,
+                                   const Timing& time) {
+                    executionStatus = error;
+                    EXPECT_EQ(UINT64_MAX, time.timeOnDevice);
+                    EXPECT_EQ(UINT64_MAX, time.timeInDriver);
+                    if (executionStatus != ErrorStatus::NONE) {
+                        EXPECT_EQ(shapes.size(), 0);
+                    }
+                });
+        EXPECT_TRUE(ret.isOk());
+        return executionStatus;
+    }
+
+    // TODO(xusongw): Add executeFenced.
+
+    const Executor kExecutor = std::get<Executor>(GetParam());
+};
+
+TEST_P(MemoryDomainExecutionTest, InvalidToken) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    if (preparedModel == nullptr) return;
+
+    Request::MemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool badDeviceMemory1 = createDeviceMemoryPool(0);    // Invalid token.
+    Request::MemoryPool badDeviceMemory2 = createDeviceMemoryPool(100);  // Unknown token.
+    RequestArgument sharedMemoryArg = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, badDeviceMemory1}},
+                  ErrorStatus::INVALID_ARGUMENT);
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, badDeviceMemory2}},
+                  ErrorStatus::INVALID_ARGUMENT);
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, badDeviceMemory1}},
+                  ErrorStatus::INVALID_ARGUMENT);
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, badDeviceMemory2}},
+                  ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidPreparedModel) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+    auto badPreparedModel = createConvPreparedModel(kTestOperand);
+    if (badPreparedModel == nullptr) return;
+
+    Request::MemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(token);
+    RequestArgument sharedMemoryArg = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+    // This should fail, because the buffer is not allocated for badPreparedModel.
+    initializeDeviceMemory(buffer);
+    testExecution(badPreparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+    testExecution(badPreparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidIOIndex) {
+    auto preparedModel = createConvPreparedModel(kTestOperand, 2);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {});
+    if (buffer == nullptr) return;
+
+    Request::MemoryPool sharedMemory1 = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool sharedMemory2 = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool sharedMemory3 = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(token);
+    RequestArgument sharedMemoryArg1 = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument sharedMemoryArg2 = {
+            .location = {.poolIndex = 1, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument sharedMemoryArg3 = {
+            .location = {.poolIndex = 2, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 3}};
+
+    // This should fail, because the device memory is not allocated for input 1.
+    initializeDeviceMemory(buffer);
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg1, deviceMemoryArg},
+                   .outputs = {sharedMemoryArg2, sharedMemoryArg3},
+                   .pools = {sharedMemory1, sharedMemory2, sharedMemory3, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    // This should fail, because the device memory is not allocated for output 1.
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg1, sharedMemoryArg2},
+                   .outputs = {sharedMemoryArg3, deviceMemoryArg},
+                   .pools = {sharedMemory1, sharedMemory2, sharedMemory3, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidIOType) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [inputBuffer, inputToken] = allocateBuffer(preparedModel, {0}, {});
+    auto [outputBuffer, outputToken] = allocateBuffer(preparedModel, {}, {0});
+    if (inputBuffer == nullptr || outputBuffer == nullptr) return;
+
+    Request::MemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(inputToken);
+    RequestArgument sharedMemoryArg = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+    // This should fail, because the device memory is allocated for input but used as output.
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    // This should fail, because the device memory is allocated for output but used as input.
+    deviceMemory.token(outputToken);
+    initializeDeviceMemory(outputBuffer);
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+}
+
+TEST_P(MemoryDomainExecutionTest, UninitializedMemory) {
+    auto preparedModel = createConvPreparedModel(kTestOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0});
+    if (buffer == nullptr) return;
+
+    Request::MemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(token);
+    RequestArgument sharedMemoryArg = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+
+    // This should fail, because the device memory is not initialized.
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::GENERAL_FAILURE);
+
+    // This should initialize the device memory.
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::NONE);
+
+    // Test again with initialized device memory.
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainExecutionTest, SameRequestMultipleRoles) {
+    auto preparedModel = createConvPreparedModel(kTestOperand, 2);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0, 1}, {0, 1});
+    if (buffer == nullptr) return;
+
+    Request::MemoryPool sharedMemory1 = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool sharedMemory2 = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(token);
+    RequestArgument sharedMemoryArg1 = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument sharedMemoryArg2 = {
+            .location = {.poolIndex = 1, .offset = 0, .length = kTestOperandDataSize}};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 2}};
+
+    // This should fail, because the same device memory cannot be used for both input and output.
+    initializeDeviceMemory(buffer);
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg, sharedMemoryArg1},
+                   .outputs = {deviceMemoryArg, sharedMemoryArg2},
+                   .pools = {sharedMemory1, sharedMemory2, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    // This should fail, because the same device memory cannot be used for multiple outputs.
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg1, sharedMemoryArg2},
+                   .outputs = {deviceMemoryArg, deviceMemoryArg},
+                   .pools = {sharedMemory1, sharedMemory2, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    // The same device memory can be used for multiple inputs.
+    initializeDeviceMemory(buffer);
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArg, deviceMemoryArg},
+                   .outputs = {sharedMemoryArg1, sharedMemoryArg2},
+                   .pools = {sharedMemory1, sharedMemory2, deviceMemory}},
+                  ErrorStatus::NONE);
+}
+
+TEST_P(MemoryDomainExecutionTest, InvalidDimensions) {
+    TestOperand testOperand = kTestOperand;
+    testOperand.dimensions[0] = 0;
+    auto preparedModel = createConvPreparedModel(testOperand);
+    auto [buffer, token] = allocateBuffer(preparedModel, {0}, {0}, kTestOperand.dimensions);
+    if (buffer == nullptr) return;
+
+    Request::MemoryPool sharedMemory = createSharedMemoryPool(kTestOperandDataSize);
+    Request::MemoryPool deviceMemory = createDeviceMemoryPool(token);
+    auto badDimensions = kTestOperand.dimensions;
+    badDimensions[0] = 2;
+    RequestArgument sharedMemoryArg = {
+            .location = {.poolIndex = 0, .offset = 0, .length = kTestOperandDataSize},
+            .dimensions = badDimensions};
+    RequestArgument deviceMemoryArg = {.location = {.poolIndex = 1}};
+    RequestArgument deviceMemoryArgWithBadDimensions = {.location = {.poolIndex = 1},
+                                                        .dimensions = badDimensions};
+
+    initializeDeviceMemory(buffer);
+    testExecution(preparedModel,
+                  {.inputs = {deviceMemoryArgWithBadDimensions},
+                   .outputs = {sharedMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArgWithBadDimensions},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::INVALID_ARGUMENT);
+
+    testExecution(preparedModel,
+                  {.inputs = {sharedMemoryArg},
+                   .outputs = {deviceMemoryArg},
+                   .pools = {sharedMemory, deviceMemory}},
+                  ErrorStatus::GENERAL_FAILURE);
+}
+
+const auto kExecutorChoices = testing::Values(Executor::ASYNC, Executor::SYNC);
+
+std::string printMemoryDomainExecutionTest(
+        const testing::TestParamInfo<MemoryDomainExecutionTestParam>& info) {
+    const auto& [namedDevice, operandType, executor] = info.param;
+    const std::string type = toString(static_cast<OperandType>(operandType));
+    const std::string executorStr = toString(executor);
+    return gtestCompliantName(getName(namedDevice) + "_" + type + "_" + executorStr);
+}
+
+INSTANTIATE_TEST_CASE_P(TestMemoryDomain, MemoryDomainExecutionTest,
+                        testing::Combine(kNamedDeviceChoices, kTestOperandTypeChoices,
+                                         kExecutorChoices),
+                        printMemoryDomainExecutionTest);
+
+}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
index 5b07034..60ceb7e 100644
--- a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
@@ -192,4 +192,19 @@
     return IPreparedModel::castFrom(preparedModelV1_0).withDefault(nullptr);
 }
 
+std::string toString(Executor executor) {
+    switch (executor) {
+        case Executor::ASYNC:
+            return "ASYNC";
+        case Executor::SYNC:
+            return "SYNC";
+        case Executor::BURST:
+            return "BURST";
+        case Executor::FENCED:
+            return "FENCED";
+        default:
+            CHECK(false);
+    }
+}
+
 }  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
index 4e51052..de082c3 100644
--- a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.h
@@ -52,6 +52,10 @@
 // Utility function to get PreparedModel from callback and downcast to V1_2.
 sp<IPreparedModel> getPreparedModel_1_3(const sp<implementation::PreparedModelCallback>& callback);
 
+enum class Executor { ASYNC, SYNC, BURST, FENCED };
+
+std::string toString(Executor executor);
+
 }  // namespace android::hardware::neuralnetworks::V1_3::vts::functional
 
 #endif  // ANDROID_HARDWARE_NEURALNETWORKS_V1_3_VTS_HAL_NEURALNETWORKS_H