[QNN EP] Update QNN to v2.13 (#17079)

### Description
Update QNN SDK to v2.13, update some UTs accordingly

onnxruntime/test/providers/qnn/batch_norm_htp_test.cc

@@ -175,6 +175,12 @@ static void RunBatchNormQDQTest(const TestInputDef<float>& input_def,
 // TODO: FIX TRANSLATION!!!
 // Check that QNN compiles DQ -> BatchNormalization -> Q as a single unit.
 // Use an input of rank 3.
+// QNN v2.13
+// Inaccuracy detected for output 'output', element 4.
+// Output quant params: scale=0.019084848463535309, zero_point=9.
+// Expected val: 1.7755576372146606
+// QNN QDQ val: 2.9963212013244629 (err 1.2207635641098022)
+// CPU QDQ val: 0.82064849138259888 (err 0.95490914583206177)
 TEST_F(QnnHTPBackendTests, DISABLED_BatchNorm1D) {
   constexpr int64_t num_channels = 2;
 
@@ -187,6 +193,12 @@ TEST_F(QnnHTPBackendTests, DISABLED_BatchNorm1D) {
 // TODO: FIX TRANSLATION!!!
 // Check that QNN compiles DQ -> BatchNormalization -> Q as a single unit.
 // Use an input of rank 4.
+// QNN v2.13
+// Inaccuracy detected for output 'output', element 14.
+// Output quant params: scale=0.023071292787790298, zero_point=19.
+// Expected val: 2.8554618358612061
+// QNN QDQ val: 5.3294687271118164 (err 2.4740068912506104)
+// CPU QDQ val: 1.6611330509185791 (err 1.194328784942627)
 TEST_F(QnnHTPBackendTests, DISABLED_BatchNorm2D) {
   constexpr int64_t num_channels = 2;
   std::vector<float> input_data = {-8.0f, -6.0f, -4.0f, -2.0f, 0.0f, 1.1f, 3.3f, 8.0f,

onnxruntime/test/providers/qnn/conv_test.cc

@@ -225,7 +225,7 @@ static void RunHTPConvOpTest(const std::string& conv_op_type, const TestInputDef
 
 // Check that QNN compiles DQ -> Conv -> Q as a single unit.
 // Tests bias as a dynamic input.
-// TODO: Segfaults when calling graphFinalize().
+// TODO: Segfaults when calling graphFinalize(). v2.13
 TEST_F(QnnCPUBackendTests, DISABLED_Convf32_dynamic_bias) {
   RunCPUConvOpTest("Conv",
                    TestInputDef<float>({1, 1, 3, 3}, false, 0.0f, 10.0f),  // Random dynamic input
@@ -575,8 +575,7 @@ TEST_F(QnnHTPBackendTests, ConvTranspose1DU8U8S32_AutoPadLower) {
                             13);
 }
 
-// TODO: re-enable tests once HTP issues are resolved
-TEST_F(QnnHTPBackendTests, DISABLED_ConvU8U8S32_large_input1_padding_bias_initializer) {
+TEST_F(QnnHTPBackendTests, ConvU8U8S32_large_input1_padding_bias_initializer) {
   RunHTPConvOpTest<uint8_t>("Conv",
                             TestInputDef<float>({1, 3, 60, 452}, false, 0.f, 10.f),        // Dynamic input
                             TestInputDef<float>({16, 3, 3, 3}, true, -1.f, 1.f),           // Static weights
@@ -588,7 +587,7 @@ TEST_F(QnnHTPBackendTests, DISABLED_ConvU8U8S32_large_input1_padding_bias_initia
                             ExpectedEPNodeAssignment::All);
 }
 
-TEST_F(QnnHTPBackendTests, DISABLED_ConvU8S32_large_input2_bias_initializer) {
+TEST_F(QnnHTPBackendTests, ConvU8S32_large_input2_bias_initializer) {
   RunHTPConvOpTest<uint8_t>("Conv",
                             TestInputDef<float>({1, 128, 8, 56}, false, 0.f, 10.f),  // Dynamic input
                             TestInputDef<float>({32, 128, 1, 1}, true, -1.f, 1.f),   // Random static weights
@@ -600,8 +599,7 @@ TEST_F(QnnHTPBackendTests, DISABLED_ConvU8S32_large_input2_bias_initializer) {
                             ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
-TEST_F(QnnHTPBackendTests, DISABLED_ConvU8U8S32_LargeInput_Dilations_Pads) {
+TEST_F(QnnHTPBackendTests, ConvU8U8S32_LargeInput_Dilations_Pads) {
   RunHTPConvOpTest<uint8_t>("Conv",
                             TestInputDef<float>({1, 3, 768, 1152}, false, 0.f, 10.f),  // Dynamic input
                             TestInputDef<float>({64, 3, 7, 7}, true, -1.f, 1.f),       // Random static weights

onnxruntime/test/providers/qnn/layer_norm_test.cc

@@ -122,13 +122,13 @@ static void RunLayerNormQDQTest(const std::vector<int64_t>& input_shape,
 
 // Check that QNN compiles DQ -> LayerNormalization -> Q as a single unit.
 // Use an input of rank 3.
-// Failed QNN op validation: QnnDsp <E> Param[0] has incorrect Value 3
+// QNN HTP only supports axis = -1
 // TODO: Use new QDQ accuracy testing approach (see TestQDQModelAccuracy)
 TEST_F(QnnHTPBackendTests, TestQDQLayerNorm1DAxis0) {
   RunLayerNormQDQTest({1, 2, 3}, {1, 2, 3}, ExpectedEPNodeAssignment::None);
 }
 
-// Failed QNN FinalizeGraphs: QnnDsp <E> Failed to finalize graph (id: 1) with err 1002
+// QNN v2.13: Failed QNN FinalizeGraphs: QnnDsp <E> Failed to finalize graph (id: 1) with err 1002
 //
 // TODO: Use new QDQ accuracy testing approach (see TestQDQModelAccuracy)
 TEST_F(QnnHTPBackendTests, DISABLED_TestQDQLayerNorm1DAxis2) {

onnxruntime/test/providers/qnn/max_pool_test.cpp

@@ -128,7 +128,7 @@ TEST_F(QnnCPUBackendTests, MaxPool_Large_Input) {
                    ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
+// QNN v2.13, backendValidateOpConfig() failed for node `MaxPool` of type `PoolMax2d` with error code 4003
 TEST_F(QnnCPUBackendTests, DISABLED_MaxPool_Ceil) {
   RunMaxPoolOpTest(TestInputDef<float>({1, 2, 3, 3}, false, -10.0f, 10.0f),  // Dynamic input with range [-10, 10]
                    {utils::MakeAttribute("kernel_shape", std::vector<int64_t>{3, 3}),
@@ -141,7 +141,7 @@ TEST_F(QnnCPUBackendTests, DISABLED_MaxPool_Ceil) {
                    ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
+// QNN v2.13, backendValidateOpConfig() failed for node `MaxPool` of type `PoolMax2d` with error code 4003
 TEST_F(QnnCPUBackendTests, DISABLED_MaxPool_Large_Input2_Ceil) {
   RunMaxPoolOpTest(TestInputDef<float>({1, 128, 16, 113}, false, -10.0f, 10.0f),  // Dynamic input with range [-10, 10]
                    {utils::MakeAttribute("kernel_shape", std::vector<int64_t>{2, 2}),
@@ -171,8 +171,7 @@ TEST_F(QnnHTPBackendTests, MaxPool_Global_HTP_u8) {
                                ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
-TEST_F(QnnHTPBackendTests, DISABLED_MaxPool_Large_Input_HTP_u8) {
+TEST_F(QnnHTPBackendTests, MaxPool_Large_Input_HTP_u8) {
   RunQDQMaxPoolOpTest<uint8_t>(TestInputDef<float>({1, 125, 8, 56}, false, -10.0f, 10.0f),  // Dynamic input with range [-10, 10]
                                {utils::MakeAttribute("kernel_shape", std::vector<int64_t>{2, 2}),
                                 utils::MakeAttribute("strides", std::vector<int64_t>{2, 2}),
@@ -196,7 +195,11 @@ TEST_F(QnnHTPBackendTests, MaxPool_Ceil_HTP_u8) {
                                ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
+// QNN v2.13: Inaccuracy detected for output 'output', element 58367.
+// Output quant params: scale=0.078431375324726105, zero_point=127.
+// Expected val: 5.6846914291381836
+// QNN QDQ val: -5.3333334922790527 (err 11.018024444580078)
+// CPU QDQ val: 5.6470589637756348 (err 0.037632465362548828)
 TEST_F(QnnHTPBackendTests, DISABLED_MaxPool_Large_Input2_Ceil_HTP_u8) {
   RunQDQMaxPoolOpTest<uint8_t>(TestInputDef<float>({1, 128, 16, 113}, false, -10.0f, 10.0f),  // Dynamic input with range [-10, 10]
                                {utils::MakeAttribute("kernel_shape", std::vector<int64_t>{2, 2}),
@@ -209,7 +212,7 @@ TEST_F(QnnHTPBackendTests, DISABLED_MaxPool_Large_Input2_Ceil_HTP_u8) {
                                ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
+// QNN v2.13: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
 TEST_F(QnnHTPBackendTests, DISABLED_MaxPool_LargeInput_1Pads) {
   RunQDQMaxPoolOpTest<uint8_t>(TestInputDef<float>({1, 64, 384, 576}, false, -10.0f, 10.0f),  // Dynamic input with range [-10, 10]
                                {utils::MakeAttribute("kernel_shape", std::vector<int64_t>{3, 3}),

onnxruntime/test/providers/qnn/resize_test.cc

@@ -178,8 +178,6 @@ static void RunQDQResizeOpTest(const TestInputDef<float>& input_def,
 // CPU tests:
 //
 
-// TODO: Enable QnnCPU tests that use "nearest" mode.
-//
 // Our non-quantized implementation of Resize uses QNN's ResizeNearestNeighbor operator,
 // which is __not__ equivalent to ONNX's Resize operator with a single specific "nearest_mode".
 // The following disabled unit tests would pass if we removed the check in QNN EP that expects the
@@ -197,9 +195,11 @@ TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestHalfPixel_rpf) {
                      ExpectedEPNodeAssignment::All);
 }
 
+// QNN v2.13 Failed for Linux
+#if defined(_WIN32)
 // Upsample that uses "round_prefer_ceil" as the "nearest_mode".
 // coordinate_transformation_mode: "half_pixel"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestHalfPixel_rpc) {
+TEST_F(QnnCPUBackendTests, ResizeUpsampleNearestHalfPixel_rpc) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 7, 5}, "nearest", "half_pixel", "round_prefer_ceil",
                      ExpectedEPNodeAssignment::All);
@@ -207,7 +207,7 @@ TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestHalfPixel_rpc) {
 
 // Downsample that uses "round_prefer_ceil" as the "nearest_mode".
 // coordinate_transformation_mode: "half_pixel"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestHalfPixel_rpc) {
+TEST_F(QnnCPUBackendTests, ResizeDownsampleNearestHalfPixel_rpc) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 1, 3}, "nearest", "half_pixel", "round_prefer_ceil",
                      ExpectedEPNodeAssignment::All);
@@ -215,23 +215,27 @@ TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestHalfPixel_rpc) {
 
 // Downsample that uses "round_prefer_floor" as the "nearest_mode".
 // coordinate_transformation_mode: "half_pixel"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestHalfPixel_rpf) {
+TEST_F(QnnCPUBackendTests, ResizeDownsampleNearestHalfPixel_rpf) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 1, 2}, "nearest", "half_pixel", "round_prefer_ceil",
                      ExpectedEPNodeAssignment::All);
 }
+#endif
 
 // Upsample that uses "round_prefer_floor" as the "nearest_mode".
 // coordinate_transformation_mode: "align_corners"
+// QNN v2.13: index #50 don't match, which is 4.67152 from -1.93515
 TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestAlignCorners_rpf) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 2, 7, 5}, false, -10.0f, 10.0f),
                      {1, 2, 21, 10}, "nearest", "align_corners", "round_prefer_floor",
                      ExpectedEPNodeAssignment::All);
 }
 
+// QNN v2.13 Failed for Linux
+#if defined(_WIN32)
 // Upsample that uses "round_prefer_ceil" as the "nearest_mode".
 // coordinate_transformation_mode: "align_corners"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestAlignCorners_rpc) {
+TEST_F(QnnCPUBackendTests, ResizeUpsampleNearestAlignCorners_rpc) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 7, 5}, "nearest", "align_corners", "round_prefer_ceil",
                      ExpectedEPNodeAssignment::All);
@@ -239,7 +243,7 @@ TEST_F(QnnCPUBackendTests, DISABLED_ResizeUpsampleNearestAlignCorners_rpc) {
 
 // Downsample that uses "round_prefer_ceil" as the "nearest_mode".
 // coordinate_transformation_mode: "align_corners"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestAlignCorners_rpc) {
+TEST_F(QnnCPUBackendTests, ResizeDownsampleNearestAlignCorners_rpc) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 1, 3}, "nearest", "align_corners", "round_prefer_ceil",
                      ExpectedEPNodeAssignment::All);
@@ -247,11 +251,12 @@ TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestAlignCorners_rpc) {
 
 // Downsample that uses "round_prefer_floor" as the "nearest_mode".
 // coordinate_transformation_mode: "align_corners"
-TEST_F(QnnCPUBackendTests, DISABLED_ResizeDownsampleNearestAlignCorners_rpf) {
+TEST_F(QnnCPUBackendTests, ResizeDownsampleNearestAlignCorners_rpf) {
   RunCPUResizeOpTest(TestInputDef<float>({1, 1, 2, 4}, false, -10.0f, 10.0f),
                      {1, 1, 1, 2}, "nearest", "align_corners", "round_prefer_floor",
                      ExpectedEPNodeAssignment::All);
 }
+#endif
 
 //
 // Cpu tests that use the "linear" mode.
@@ -309,10 +314,11 @@ TEST_F(QnnHTPBackendTests, ResizeU8_2xNearestAsymmetricFloor) {
 // QNN's own Resize operator (instead of ResizeNearestNeighbor), but it doesn't support the "asymmetric" coordinate
 // transform mode.
 //
-// Expected: contains 192 values, where each value and its corresponding value in 16-byte object
-// <C0-00 00-00 00-00 00-00 40-05 D6-27 BB-01 00-00> are an almost-equal pair
-// Actual : 16 - byte object<C0 - 00 00 - 00 00 - 00 00 - 00 40 - 04 E9 - 1B BB - 01 00 - 00>,
-// where the value pair(0.15, 0.501) at index #1 don't match, which is 0.351 from 0.15
+// QNN v2.13: Inaccuracy detected for output 'output', element 189.
+// Output quant params: scale=0.078431375324726105, zero_point=127.
+// Expected val: -2.663428783416748
+// QNN QDQ val: 7.4509806632995605 (err 10.114409446716309)
+// CPU QDQ val: -2.6666667461395264 (err 0.0032379627227783203)
 TEST_F(QnnHTPBackendTests, DISABLED_ResizeU8_2xNearestAsymmetricCeil) {
   RunQDQResizeOpTest<uint8_t>(TestInputDef<float>({1, 3, 4, 4}, false, -10.0f, 10.0f),
                               {1, 3, 8, 8}, "nearest", "asymmetric", "ceil",

onnxruntime/test/providers/qnn/simple_op_htp_test.cc

@@ -244,17 +244,9 @@ TEST_F(QnnHTPBackendTests, UnaryOp_Cos) {
                     11, ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Inaccuracy when computing cos(-1.88436)
-//
-// cos(-1.88436f) fp32 cpu ep = -0.308450460
-// cos(-1.88436f) qdq cpu ep  = -0.298039228
-// cos(-1.88436f) qdq QNN ep  = -0.321568638
-//
-// QNN error: 0.013118177652359009, CPU error: 0.010411232709884644
-//
-// input quant params: scale=0.0246399231, zero_point=127
-// output quant params: scale=0.00784313772, zero_point=127
-TEST_F(QnnHTPBackendTests, DISABLED_UnaryOp_Cos_Inaccurate) {
+// Check that QNN compiles DQ -> Cos -> Q as a single unit.
+// Use an input of rank 3.
+TEST_F(QnnHTPBackendTests, UnaryOp_Cos_Inaccurate) {
   RunQDQUnaryOpTest(TestInputDef<float>({1, 2, 3}, false, {-3.14159f, -1.88436f, -0.542863f, 0.0f, 1.05622f, 3.14159f}),
                     "Cos", {},
                     11, ExpectedEPNodeAssignment::All);
@@ -449,7 +441,7 @@ TEST_F(QnnHTPBackendTests, BinaryOp_Sub4D) {
 
 // TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
 // Enable when this is fixed.
-TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Sub4D_LargeInputs) {
+TEST_F(QnnHTPBackendTests, BinaryOp_Sub4D_LargeInputs) {
   RunQDQBinaryOpTest<uint8_t>("Sub", TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
                               TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
                               17, ExpectedEPNodeAssignment::All);
@@ -457,7 +449,7 @@ TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Sub4D_LargeInputs) {
 
 // TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
 // Enable when this is fixed.
-TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Sub4D_Broadcast) {
+TEST_F(QnnHTPBackendTests, BinaryOp_Sub4D_Broadcast) {
   RunQDQBinaryOpTest<uint8_t>("Sub", TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
                               TestInputDef<float>({3, 1, 1}, true, {1.0f, 0.5f, -0.3f}),
                               17, ExpectedEPNodeAssignment::All);
@@ -470,8 +462,12 @@ TEST_F(QnnHTPBackendTests, BinaryOp_Div4D_SmallInputs) {
                               17, ExpectedEPNodeAssignment::All);
 }
 
-// TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
-// Enable when this is fixed.
+// TODO: Enable when this is fixed.
+// QNN v2.13: Inaccuracy detected for output 'output', element 2551923.
+// Output quant params: scale=4100.92626953125, zero_point=126.
+// Expected val: -277957.3125
+// QNN QDQ val: 0 (err 277957.3125)
+// CPU QDQ val: -516716.71875 (err 238759.40625)
 TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Div4D_LargeInputs) {
   RunQDQBinaryOpTest<uint8_t>("Div", TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
                               TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
@@ -481,7 +477,7 @@ TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Div4D_LargeInputs) {
 // TODO: Certain large input sizes cause the QNN graph to fail to finalize with error 1002 (QNN_COMMON_ERROR_MEM_ALLOC).
 // Enable when this is fixed.
 // Fails accuracy when input0 has dims [1,3,768,768]
-TEST_F(QnnHTPBackendTests, DISABLED_BinaryOp_Div4D_Broadcast) {
+TEST_F(QnnHTPBackendTests, BinaryOp_Div4D_Broadcast) {
   RunQDQBinaryOpTest<uint8_t>("Div", TestInputDef<float>({1, 3, 768, 1152}, false, -1.0f, 1.0f),
                               TestInputDef<float>({3, 1, 1}, true, {1.0f, 0.5f, -0.3f}),
                               17, ExpectedEPNodeAssignment::All);

tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml

@@ -3,7 +3,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: qnn-v2.12.0.230626
+  default: qnn-v2.13.1.230730
 
 jobs:
 - job: Build_QNN_EP

tools/ci_build/github/azure-pipelines/linux-qnn-ci-pipeline.yml

@@ -3,7 +3,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: qnn-v2.12.0.230626
+  default: qnn-v2.13.1.230730
 
 jobs:
   - job: Build_QNN_EP

tools/ci_build/github/azure-pipelines/qnn-ep-nuget-packaging-pipeline.yml

@@ -2,12 +2,12 @@ parameters:
 - name: qnn_sdk_path_win
   displayName: QNN Windows SDK path
   type: string
-  default: C:\data\qnnsdk\qnn-v2.12.1.230626_win
+  default: C:\data\qnnsdk\qnn-v2.13.1.230730_win
 
 - name: qnn_sdk_info
   displayName: QNN SDK Version Information
   type: string
-  default: qnn-v2.12.1.230626_win
+  default: qnn-v2.13.1.230730_win
 
 - name: ort_package_version
   displayName: OnnxRuntime Nuget package version

tools/ci_build/github/azure-pipelines/win-qnn-arm64-ci-pipeline.yml

@@ -3,7 +3,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: qnn-v2.12.1.230626_win
+  default: qnn-v2.13.1.230730_win
 
 jobs:
 - job: 'build'

tools/ci_build/github/azure-pipelines/win-qnn-ci-pipeline.yml

@@ -3,7 +3,7 @@ parameters:
 - name: QnnSdk
   displayName: QNN SDK version
   type: string
-  default: qnn-v2.12.1.230626_win
+  default: qnn-v2.13.1.230730_win
 
 jobs:
 - job: 'build'