diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index ab524c2..7c5b0bc 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -88,11 +88,22 @@
                                                 sp<ExecutionCallback>& callback) {
     return preparedModel->execute_1_2(request, callback);
 }
+static Return<ErrorStatus> ExecutePreparedModel(sp<V1_0::IPreparedModel>&, const Request&) {
+    ADD_FAILURE() << "asking for synchronous execution at V1_0";
+    return ErrorStatus::GENERAL_FAILURE;
+}
+static Return<ErrorStatus> ExecutePreparedModel(sp<V1_2::IPreparedModel>& preparedModel,
+                                                const Request& request) {
+    return preparedModel->executeSynchronously(request);
+}
+enum class Synchronously { NO, YES };
+const float kDefaultAtol = 1e-5f;
+const float kDefaultRtol = 1e-5f;
 template <typename T_IPreparedModel>
 void EvaluatePreparedModel(sp<T_IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
                            const std::vector<MixedTypedExample>& examples,
-                           bool hasRelaxedFloat32Model = false, float fpAtol = 1e-5f,
-                           float fpRtol = 1e-5f) {
+                           bool hasRelaxedFloat32Model = false, float fpAtol = kDefaultAtol,
+                           float fpRtol = kDefaultRtol, Synchronously sync = Synchronously::NO) {
     const uint32_t INPUT = 0;
     const uint32_t OUTPUT = 1;
 
@@ -185,19 +196,31 @@
         inputMemory->commit();
         outputMemory->commit();
 
-        // launch execution
-        sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-        ASSERT_NE(nullptr, executionCallback.get());
-        Return<ErrorStatus> executionLaunchStatus = ExecutePreparedModel(
-            preparedModel, {.inputs = inputs_info, .outputs = outputs_info, .pools = pools},
-            executionCallback);
-        ASSERT_TRUE(executionLaunchStatus.isOk());
-        EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+        if (sync == Synchronously::NO) {
+            SCOPED_TRACE("asynchronous");
 
-        // retrieve execution status
-        executionCallback->wait();
-        ErrorStatus executionReturnStatus = executionCallback->getStatus();
-        EXPECT_EQ(ErrorStatus::NONE, executionReturnStatus);
+            // launch execution
+            sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+            ASSERT_NE(nullptr, executionCallback.get());
+            Return<ErrorStatus> executionLaunchStatus = ExecutePreparedModel(
+                preparedModel, {.inputs = inputs_info, .outputs = outputs_info, .pools = pools},
+                executionCallback);
+            ASSERT_TRUE(executionLaunchStatus.isOk());
+            EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+
+            // retrieve execution status
+            executionCallback->wait();
+            ErrorStatus executionReturnStatus = executionCallback->getStatus();
+            EXPECT_EQ(ErrorStatus::NONE, executionReturnStatus);
+        } else {
+            SCOPED_TRACE("synchronous");
+
+            // execute
+            Return<ErrorStatus> executionStatus = ExecutePreparedModel(
+                preparedModel, {.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
+            ASSERT_TRUE(executionStatus.isOk());
+            EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionStatus));
+        }
 
         // validate results
         outputMemory->read();
@@ -215,6 +238,13 @@
         }
     }
 }
+template <typename T_IPreparedModel>
+void EvaluatePreparedModel(sp<T_IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
+                           const std::vector<MixedTypedExample>& examples,
+                           bool hasRelaxedFloat32Model, Synchronously sync) {
+    EvaluatePreparedModel(preparedModel, is_ignored, examples, hasRelaxedFloat32Model, kDefaultAtol,
+                          kDefaultRtol, sync);
+}
 
 static void getPreparedModel(sp<PreparedModelCallback> callback,
                              sp<V1_0::IPreparedModel>* preparedModel) {
@@ -362,7 +392,9 @@
     ASSERT_NE(nullptr, preparedModel.get());
 
     EvaluatePreparedModel(preparedModel, is_ignored, examples,
-                          model.relaxComputationFloat32toFloat16);
+                          model.relaxComputationFloat32toFloat16, Synchronously::NO);
+    EvaluatePreparedModel(preparedModel, is_ignored, examples,
+                          model.relaxComputationFloat32toFloat16, Synchronously::YES);
 }
 
 }  // namespace generated_tests
diff --git a/neuralnetworks/1.2/IPreparedModel.hal b/neuralnetworks/1.2/IPreparedModel.hal
index 5590487..4e91c67 100644
--- a/neuralnetworks/1.2/IPreparedModel.hal
+++ b/neuralnetworks/1.2/IPreparedModel.hal
@@ -51,8 +51,9 @@
      * and complete successfully (ErrorStatus::NONE). There must be
      * no failure unless the device itself is in a bad state.
      *
-     * Multiple threads can call the execute_1_2 function on the same IPreparedModel
-     * object concurrently with different requests.
+     * Any number of calls to the execute, execute_1_2, and executeSynchronously
+     * functions, in any combination, may be made concurrently, even on the same
+     * IPreparedModel object.
      *
      * @param request The input and output information on which the prepared
      *                model is to be executed.
@@ -71,4 +72,39 @@
      */
     execute_1_2(Request request, IExecutionCallback callback)
         generates (ErrorStatus status);
+
+    /**
+     * Performs a synchronous execution on a prepared model.
+     *
+     * The execution is performed synchronously with respect to the caller.
+     * executeSynchronously must verify the inputs to the function are
+     * correct. If there is an error, executeSynchronously must immediately
+     * return with the appropriate ErrorStatus value. If the inputs to the
+     * function are valid and there is no error, executeSynchronously must
+     * perform the execution, and must not return until the execution is
+     * complete.
+     *
+     * If the prepared model was prepared from a model wherein all tensor
+     * operands have fully specified dimensions, and the inputs to the function
+     * are valid, then the execution should complete successfully
+     * (ErrorStatus::NONE). There must be no failure unless the device itself is
+     * in a bad state.
+     *
+     * Any number of calls to the execute, execute_1_2, and executeSynchronously
+     * functions, in any combination, may be made concurrently, even on the same
+     * IPreparedModel object.
+     *
+     * @param request The input and output information on which the prepared
+     *                model is to be executed.
+     * @return status Error status of the execution, must be:
+     *                - NONE if execution is performed successfully
+     *                - DEVICE_UNAVAILABLE if driver is offline or busy
+     *                - GENERAL_FAILURE if there is an unspecified error
+     *                - OUTPUT_INSUFFICIENT_SIZE if provided output buffer is
+     *                  not large enough to store the resultant values
+     *                - INVALID_ARGUMENT if one of the input arguments is
+     *                  invalid
+     */
+    executeSynchronously(Request request)
+        generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
index e2722aa..d80fbcf 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
@@ -97,18 +97,29 @@
 static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
                      Request request, const std::function<void(Request*)>& mutation) {
     mutation(&request);
-    SCOPED_TRACE(message + " [execute]");
 
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus =
-        preparedModel->execute_1_2(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+    {
+        SCOPED_TRACE(message + " [execute_1_2]");
 
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+        sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+        ASSERT_NE(nullptr, executionCallback.get());
+        Return<ErrorStatus> executeLaunchStatus =
+            preparedModel->execute_1_2(request, executionCallback);
+        ASSERT_TRUE(executeLaunchStatus.isOk());
+        ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+        executionCallback->wait();
+        ErrorStatus executionReturnStatus = executionCallback->getStatus();
+        ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+    }
+
+    {
+        SCOPED_TRACE(message + " [executeSynchronously]");
+
+        Return<ErrorStatus> executeStatus = preparedModel->executeSynchronously(request);
+        ASSERT_TRUE(executeStatus.isOk());
+        ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeStatus));
+    }
 }
 
 // Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,