Running the same model in TF and TFLiteMicro produces different outputs
hi @kratmanski-logi ,

Can you check this [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/core.py) and let me know if this is the one you were looking for. Hi,
I checked your link, the core.py seem to have a Dense class reference, however it does not tell me how exactly 8x8bit multiplication (data X weights), adding 32bit bias, and rescaling is happening, on the same detail as in link I provided for Micro side (that line 65 for fully connected). My model is fixed point 8x8 but data X weights, 32bit bias, and since fixed point there is rescaling/zero centering before passing to the next layer. I need to see the reference fixed point computation. Thanks Hi @kratmanski-logi ,

Can you please check [this](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantized_matmul_op.cc)  and [this link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantization_utils.cc#L20) and let me know if this is the one you were looking for. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. Thank you for your reply, Sawantkumar, I will check and get back to you
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

latest

### Custom code

No

### OS platform and distribution

both Linux and Windows

### Mobile device

_No response_

### Python version

latest

### Bazel version

_No response_

### GCC/compiler version

latest

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I have trained float point tf model and converted it to 8bit .tflite model. I run this .tflite model on the same test input in two environments, both on the same PC with linux (no ):
1) in tensorflow, installed as typically in linux via "pip install tensorflow" and then imported via "import tensorflow as tf" in python
2) in gcc in linux, after converting .tflite to flat buffer, where the C reference kernels are in https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/kernels

Problem: Running the same .tflite model/same input in these two different environments produces different outputs. Given that the (very small and simple) model is already heavily quantized, I am expecting to get bit-exact output, whereas I observe the output difference that is beyond some sort of rounding error. In the process of debugging, I narrowed down to one of the kernels that produces not-bit-exact result: Fully_Connected. The source code for this kernel under environment (2) lives here (line 65):
https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/kernels/internal/reference/fully_connected.h

Main Question: Can you point me to the source code of fully_connected computations for the environment (1) so I can compare the computations to the ones for the environment (2) and figure out myself where the output difference comes from? I understand that in (2) python calls precompiled C/C++ kernels, and I would like to see the source code of fully_connected (and other standard kernels such as conv2d) that was used for pre-compiling. 

Note: I think the following code is being used in environment (1), however it is still not at the deep level where i can clearly see the matrix multiplication -> add bias -> rescale sequence of fully_connected operations that I can compare with the source code above for the environment (2)

### Standalone code to reproduce the issue

```shell
https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/kernels/internal/reference/fully_connected.h
https://github.com/sourcecode369/tensorflow-1/blob/master/tensorflow/lite/kernels/cpu_backend_gemm_ruy.h
```


### Relevant log output

_No response_