diff --git a/tensorflow/lite/micro/kernels/fully_connected_common.cc b/tensorflow/lite/micro/kernels/fully_connected_common.cc
index 5a8d312..66f8787 100644
--- a/tensorflow/lite/micro/kernels/fully_connected_common.cc
+++ b/tensorflow/lite/micro/kernels/fully_connected_common.cc
@@ -57,6 +57,24 @@
     TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter,
     const TfLiteTensor* bias, TfLiteTensor* output,
     OpDataFullyConnected* data) {
+  // TODO(b/324385802): Support per-channel quantization for FullyConnected.
+  // If you have hit this failure message, you will need to disable this
+  // behavior. This can be done by setting the following flag to true:
+  // TfLiteConverter._experimental_disable_per_channel_quantization_for_dense_layers
+  // https://github.com/tensorflow/tensorflow/blob/377f47694fa790e98db6665b9adecde00b5e0d68/tensorflow/lite/python/lite.py#L674
+  if (filter->quantization.type == kTfLiteAffineQuantization &&
+      filter->quantization.params != nullptr) {
+    TfLiteAffineQuantization* affine_quantization =
+        reinterpret_cast<TfLiteAffineQuantization*>(
+            filter->quantization.params);
+    TF_LITE_ENSURE(context, affine_quantization->scale);
+    TF_LITE_ENSURE_MSG(
+        context, affine_quantization->scale->size == 1,
+        "FullyConnected per-channel quantization not yet supported. Please set "
+        "converter._experimental_disable_per_channel_quantization_for_dense_"
+        "layers = True.");
+  }
+
   if (data_type != kTfLiteFloat32) {
     double real_multiplier = 0.0;
     TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
diff --git a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
index 3edf420..932b832 100644
--- a/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
+++ b/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h
@@ -390,9 +390,9 @@
   int state_size_[3] = {2, batch_size, state_dimension};
 
   // see lstm_shared.h for tensor names, the last tensor is the output tensor
-  TfLiteTensor tensors_[24 + 1];
+  TfLiteTensor tensors_[24 + 1] = {};
   // Use for internel kernel testing
-  TfLiteEvalTensor eval_tensors_[24 + 1];
+  TfLiteEvalTensor eval_tensors_[24 + 1] = {};
   // indices for the tensors inside the node (required by kernel runner)
   int input_tensor_indices_[1 + 24] = {};
   // single output (last in the tensors array)
diff --git a/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py b/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
index 4d80991..342f893 100644
--- a/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
+++ b/tensorflow/lite/micro/tools/requantize_flatbuffer_test.py
@@ -24,7 +24,7 @@
 from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
 
 
-#TODO(b/248061370): replace the keras model creation process with flatbuffer manipulation to speed up test
+# TODO(b/248061370): replace the keras model creation process with flatbuffer manipulation to speed up test
 def create_simple_fc_model():
   '''Create a simple model with two fully connected(fc) layers'''
   model = tf.keras.models.Sequential([
@@ -60,6 +60,8 @@
         EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
     ]
   converter.representative_dataset = representative_dataset_gen
+  # TODO(b/324385802): Support per-channel quantization for FullyConnected.
+  converter._experimental_disable_per_channel_quantization_for_dense_layers = True
   return converter.convert()