NOTE: Much of the latest development in this repo comes from claude code. It has been lightly reviewed and tested so far and various things may be broken, use the latest main branch at your peril!

Overview

Tiny-NN is a toy neural network accelerator written in system verilog. It's specifically designed for use with tiny tapeout (https://tinytapeout.com/) and for use with CNNs (convolutional neural networks) though can do generic multiply-accumulate operations to implement linear neurons.

The matching tiny tapeout repository can be found at https://github.com/GregAC/tt10-tiny-nn. sv2v is used to transform the system verilog into verilog for using with the OpenLANE flow used by tiny tapeout.

See the documentation under doc/ for further information.

Repository Structure

This repository contains the RTL (in rtl/) and DV (in dv/) for Tiny-NN and a model (in model/) written in Rust. The model isn't fully developed and relies on altering the main function to do what is required rather than providing a usable CLI application.

A makefile is used to build the various testbenches and the verilog for tiny tapeout. It's contained in the root directory and has these targets

• top_dv - Top-level testbench
• top_dv_sv2v - Same testbench as above but using the sv2v built verilog version of the RTL
• fp_add_dv - Block-level testbench for the floating point adder
• fp_mul_dv - Block-level testbench for the floating point multiplier
• sv2v - Builds verilog version of the RTL with sv2v, outputs to sv2v_out/

The rust model is built with cargo

Dependencies

• Verilator - Used to build the testbench. v5.032 is known to work, other versions may work as well, though it must be a v5 as it relies on v5 features.
• GTKWave - For viewing the .fst waves output from the testbench
• sv2v - For building a verilog version of the system verilog source
• rust - To build the model