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