CNN

This repository is an excuse to learn abount Convolutional Neural Networks by implementing one in FPGA. The main goal is to learn, and to make good use of the tools I enjoy the most for digital design. These include nmigen, cocotb, yosys, icarus verilog, gtkwave.

The main goal is to have an easy python2fpga port by generating a Verilog file from a trained nn designed with some Python lib like Keras.

The status of this project is "very" WIP!

To do (HDL):

• MACC: HDL + testbench (should be reimplemented to infer arch dependent dsp slices)
• Pipelined tree adder: HDL + testbench
• Dot product: HDL + testbench
• Dot product array (farm): HDL + testbench
• Row Fifo: HDL + testbench
• MatrixFeeder: HDL + testbench
• MatrixStream interface
• Implement MatrixStream interface in existing cores
• Convolution: HDL + testbench
• Resizer (Padder & Cropper): HDL + testbench
• Convolution Layer
• StreamWrapper for logic with clken
• Pooling: HDL + testbench
• ReLU
• Ciruclar ROM: HDL + testbench
• Stream MACC: HDL + testbench
• Sigmoid / Softmax
• MLP node
• MLP layer
• CNN (Customizable integration of the cores above)
• UART interface to be able to run some tests in hw with a low-cost fpga (only as a proof of concept)
• PC: Python Uart Tx/Rx
• python2fpga (integration with keras?)

To do (enhacements):

• Paralell convolution by splitting input image

To do (tools):

• Dockerfile
• CI
• Python package
• Example of verilog generation

To do (others):

• Organize files, interfaces, etc. (this one will remain "undone" until a more stable design is reached)
• Document the cores, usage, etc.

Requirements

There is a dockerfile available with all the required tools!

• nmigen (latest)
• yosys (v0.9+, can use yowasp-yosys)
• cocotb (recommended 1.3.1+)
• icarus verilog
• nmigen-cocotb@icarus-g2005
• pytest, pytest-repeat, pytest-timeout
• numpy
• scipy

Testing

python3 -m pytest -vs cnn/ --log-cli-level info

Intro to nn

• https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
• https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
• https://medium.com/technologymadeeasy/the-best-explanation-of-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8