NeuralNetworkOnFPGA

An initial proof of concept for a neural network on FPGA.

I created this project as a proof of concept to start working with Neural Networks on an FPGA. The goal was to demonstrate that a NN could be trained to perform a simple operation (4 input XOR, for its simplicity) and then save its learned weights and biases in a format suitable for FPGA implementation.

This exercise serves as a foundational step in exploring the implementation of machine learning models on hardware platforms like FPGAs, which can be more efficient for certain types of computations.

A consistent data format used for fixed point arithmetic this entire project:

Signed Bit	Integer Bits	Fractional Bits
1	2	13

Neural Network Brief

This neural network is a small-scale implementation designed to solve the XOR logical operation. It consists of an input layer with four neurons, a hidden layer with two neurons, and an output layer with one neuron. The network uses the sigmoid activation function to normalize input data and performs forward and backward passes during training.

Layer	Number of Neurons	Description
Input Layer	4	Input for 4 input XOR function
Hidden Layer	2	Feature Extraction for the boolean pattern
Output Layer	1	If val > 0.5 --> TRUE, else val < 0.5 --> FALSE

# Project status

Cocotb QuickStart

Shoutout to AJ Steenhoek for introducing me to this, and showing me the basics!

This project gave me an oppurtunity to try out Cocotb. It is a simulation library for writing testbenches in Python. It is easy to use, and more importantly, compatible with standard Python libraries (makes verifying DSP modules damn easy, ha ha).

I am using cocotb_test with it too, to remove the need of Makefiles. Shoutout to AJ for this too!

All the design and verification files are in the rtl directory. The reason for non-segregation into 'src' and 'sim' is because cocotb-test needs both design and verification files in the same directory.

Naming convention for	format
Design Units	<module_name>.sv
Cocotb Testbench:	<tb_module_name>.py
Cocotb-test File:	<test_module_name>.py

For Quick Reference, an example of a Cocotb test file:

Create a <test_module_name>.py file:

from cocotb_test.simulator import run
def test_fix_point_adder():
    run(
        verilog_sources=["fix_point_adder.sv"], # source
        toplevel="fix_point_adder",             # top level HDL
        module="tb_fix_point_adder"             # name of cocotb test module
    )

And to run it:

SIM=icarus pytest -o log_cli=True test_sigmoid.py

I plan to take this project forward with making an FPGA implementation of an audio autoencoder in the coming weeks. Any other ideas or suggestions are much appreciated too!

satjpatel / neuralnetworkonfpga Goto Github PK