u/mjao4

I recently completed a LeNet-5 Convolutional Neural Network (CNN) hardware accelerator designed in synthesizable RTL Verilog/SystemVerilog and implemented on an A7-100T FPGA (xc7a100tcsg324-1). The CNN is trained on the MNIST handwritten digit dataset and optimized for hardware inference, achieving ∼98.2% inference accuracy with uniform Q1.7 fixed-point quantization. The design is implemented as a streaming dataflow architecture to minimize latency.

Here’s a live demo that runs at 100MHz. A python script allows a 28x28 digit frame to be drawn and sent to through the board’s USB-UART bridge. Once the FPGA receives all of the pixels and the TX LED turns off, inference begins and the predicted class displays on the board’s 7-segment display.