diff --git a/src/tim/transform/reduce_layout_inference_test.cc b/src/tim/transform/reduce_layout_inference_test.cc
new file mode 100644
index 0000000..34da5ba
--- /dev/null
+++ b/src/tim/transform/reduce_layout_inference_test.cc
@@ -0,0 +1,114 @@
+/****************************************************************************
+*
+*    Copyright (c) 2022 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops.h"
+#include "test_utils.h"
+#include "gtest/gtest.h"
+#include "tim/transform/layout_inference.h"
+#include "permute_vector.h"
+
+TEST(Reduce_Min, notalign_1_2_0) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3,1,2}); //input_pv={1,2,0}
+  tim::vx::ShapeType output_shape({2, 1});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec reduce_sum_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                      tim::vx::TensorAttribute::OUTPUT);
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto reduce_sum_out = graph->CreateTensor(reduce_sum_spec);
+  std::vector<int32_t> axis = {1};
+  auto reduce_sum =
+      graph->CreateOperation<tim::vx::ops::ReduceMin>(axis, false);
+  (*reduce_sum).BindInputs({input_tensor}).BindOutputs({reduce_sum_out});
+
+  std::vector<float> in_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
+  std::vector<float> golden = {
+      0.2, 0.4
+  };
+  std::map<std::shared_ptr<tim::vx::Tensor>,
+           std::shared_ptr<tim::transform::IPermuteVector>>
+      tensor_pv_map;
+  std::shared_ptr<tim::transform::IPermuteVector> pv =
+      std::make_shared<tim::transform::PermuteVector<3>>(
+          std::initializer_list<uint32_t>({1,2,0}));
+  tensor_pv_map.insert({input_tensor, pv});
+  auto transform = tim::transform::LayoutInference(graph, ctx, tensor_pv_map);
+  auto infer_graph = transform.first;
+  EXPECT_TRUE(infer_graph->Compile());
+  auto graph_io_map = transform.second;
+  auto infer_input = graph_io_map[graph->InputsTensor()[0]];
+  auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
+  EXPECT_TRUE(infer_input->CopyDataToTensor(in_data.data(), in_data.size()));
+  EXPECT_TRUE(infer_graph->Run());
+
+  std::vector<float> output(golden.size());
+  EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
+  EXPECT_EQ(golden, output);
+}
+
+TEST(Reduce_Min, notalign_1_0_2) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3,2,1}); //input_pv={1,0,2}
+  tim::vx::ShapeType output_shape({2, 1});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec reduce_sum_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                      tim::vx::TensorAttribute::OUTPUT);
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto reduce_sum_out = graph->CreateTensor(reduce_sum_spec);
+  std::vector<int32_t> axis = {1};
+  auto reduce_sum =
+      graph->CreateOperation<tim::vx::ops::ReduceMin>(axis, false);
+  (*reduce_sum).BindInputs({input_tensor}).BindOutputs({reduce_sum_out});
+
+  std::vector<float> in_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
+  std::vector<float> golden = {
+      0.2, 0.4
+  };
+  std::map<std::shared_ptr<tim::vx::Tensor>,
+           std::shared_ptr<tim::transform::IPermuteVector>>
+      tensor_pv_map;
+  std::shared_ptr<tim::transform::IPermuteVector> pv =
+      std::make_shared<tim::transform::PermuteVector<3>>(
+          std::initializer_list<uint32_t>({1,0,2}));
+  tensor_pv_map.insert({input_tensor, pv});
+  auto transform = tim::transform::LayoutInference(graph, ctx, tensor_pv_map);
+  auto infer_graph = transform.first;
+  EXPECT_TRUE(infer_graph->Compile());
+  auto graph_io_map = transform.second;
+  auto infer_input = graph_io_map[graph->InputsTensor()[0]];
+  auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
+  EXPECT_TRUE(infer_input->CopyDataToTensor(in_data.data(), in_data.size()));
+  EXPECT_TRUE(infer_graph->Run());
+
+  std::vector<float> output(golden.size());
+  EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
+  EXPECT_EQ(golden, output);
+}