diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc b/tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc
index a6affaa11bb..105836cb92b 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc
@@ -30,16 +30,10 @@ namespace tflite {
 
 namespace {
 
-cmsis_nn_dims FillVariableShape(int32_t rank, int32_t* tensor_dims) {
-  if (rank == 4) {
-    return {tensor_dims[0], tensor_dims[1], tensor_dims[2], tensor_dims[3]};
-  } else if (rank == 3) {
-    return {1, tensor_dims[0], tensor_dims[1], tensor_dims[2]};
-  } else if (rank == 2) {
-    return {1, 1, tensor_dims[0], tensor_dims[1]};
-  } else {
-    return {1, 1, 1, 1};
-  }
+cmsis_nn_dims FillVariableShape(const RuntimeShape& shape) {
+  RuntimeShape extended_shape = RuntimeShape::ExtendedShape(4, shape);
+  return {extended_shape.Dims(0), extended_shape.Dims(1),
+          extended_shape.Dims(2), extended_shape.Dims(3)};
 }
 
 TfLiteStatus EvalMaximum(TfLiteContext* context, TfLiteNode* node) {
@@ -55,12 +49,9 @@ TfLiteStatus EvalMaximum(TfLiteContext* context, TfLiteNode* node) {
   RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
   RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
 
-  cmsis_nn_dims input_1_dims = FillVariableShape(
-      input_1_shape.DimensionsCount(), input_1_shape.DimsData());
-  cmsis_nn_dims input_2_dims = FillVariableShape(
-      input_2_shape.DimensionsCount(), input_2_shape.DimsData());
-  cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
-                                                output_shape.DimsData());
+  cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
+  cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
+  cmsis_nn_dims output_dims = FillVariableShape(output_shape);
 
   switch (op_context.output->type) {
     case kTfLiteInt8:
@@ -107,12 +98,9 @@ TfLiteStatus EvalMaximumInt8(TfLiteContext* context, TfLiteNode* node) {
   RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
   RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
 
-  cmsis_nn_dims input_1_dims = FillVariableShape(
-      input_1_shape.DimensionsCount(), input_1_shape.DimsData());
-  cmsis_nn_dims input_2_dims = FillVariableShape(
-      input_2_shape.DimensionsCount(), input_2_shape.DimsData());
-  cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
-                                                output_shape.DimsData());
+  cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
+  cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
+  cmsis_nn_dims output_dims = FillVariableShape(output_shape);
 
   switch (op_context.output->type) {
     case kTfLiteInt8:
@@ -147,12 +135,9 @@ TfLiteStatus EvalMinimum(TfLiteContext* context, TfLiteNode* node) {
   RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
   RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
 
-  cmsis_nn_dims input_1_dims = FillVariableShape(
-      input_1_shape.DimensionsCount(), input_1_shape.DimsData());
-  cmsis_nn_dims input_2_dims = FillVariableShape(
-      input_2_shape.DimensionsCount(), input_2_shape.DimsData());
-  cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
-                                                output_shape.DimsData());
+  cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
+  cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
+  cmsis_nn_dims output_dims = FillVariableShape(output_shape);
 
   switch (op_context.output->type) {
     case kTfLiteInt8:
@@ -199,12 +184,9 @@ TfLiteStatus EvalMinimumInt8(TfLiteContext* context, TfLiteNode* node) {
   RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
   RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
 
-  cmsis_nn_dims input_1_dims = FillVariableShape(
-      input_1_shape.DimensionsCount(), input_1_shape.DimsData());
-  cmsis_nn_dims input_2_dims = FillVariableShape(
-      input_2_shape.DimensionsCount(), input_2_shape.DimsData());
-  cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
-                                                output_shape.DimsData());
+  cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
+  cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
+  cmsis_nn_dims output_dims = FillVariableShape(output_shape);
 
   switch (op_context.output->type) {
     case kTfLiteInt8:
diff --git a/tensorflow/lite/micro/kernels/cmsis_nn/pad.cc b/tensorflow/lite/micro/kernels/cmsis_nn/pad.cc
index bacba0b325b..81ffa4e07c2 100644
--- a/tensorflow/lite/micro/kernels/cmsis_nn/pad.cc
+++ b/tensorflow/lite/micro/kernels/cmsis_nn/pad.cc
@@ -49,13 +49,21 @@ TfLiteStatus PadEvalInt8(TfLiteContext* context, TfLiteNode* node) {
   int8_t* output_ptr = tflite::micro::GetTensorData<int8_t>(output);
 
   const RuntimeShape d = tflite::micro::GetTensorShape(input);
-  const cmsis_nn_dims input_size = {d.Dims(0), d.Dims(1), d.Dims(2), d.Dims(3)};
+  const int rank = d.DimensionsCount();
+
+  cmsis_nn_dims input_size = {
+      rank >= 4 ? d.Dims(rank - 4) : 1, rank >= 3 ? d.Dims(rank - 3) : 1,
+      rank >= 2 ? d.Dims(rank - 2) : 1, rank >= 1 ? d.Dims(rank - 1) : 1};
 
   const PadParams p = data->params;
-  const cmsis_nn_dims pre_pad = {p.left_padding[0], p.left_padding[1],
-                                 p.left_padding[2], p.left_padding[3]};
-  const cmsis_nn_dims post_pad = {p.right_padding[0], p.right_padding[1],
-                                  p.right_padding[2], p.right_padding[3]};
+  cmsis_nn_dims pre_pad = {rank >= 4 ? p.left_padding[rank - 4] : 0,
+                           rank >= 3 ? p.left_padding[rank - 3] : 0,
+                           rank >= 2 ? p.left_padding[rank - 2] : 0,
+                           rank >= 1 ? p.left_padding[rank - 1] : 0};
+  cmsis_nn_dims post_pad = {rank >= 4 ? p.right_padding[rank - 4] : 0,
+                            rank >= 3 ? p.right_padding[rank - 3] : 0,
+                            rank >= 2 ? p.right_padding[rank - 2] : 0,
+                            rank >= 1 ? p.right_padding[rank - 1] : 0};
 
   arm_pad_s8(input_ptr, output_ptr, pad_value, &input_size, &pre_pad,
              &post_pad);