# adam-maj/tiny-gpu

A minimal GPU design in Verilog to learn how GPUs work from the ground up

Repository: https://github.com/adam-maj/tiny-gpu
Canonical: https://ross.abutalabs.com/products/tiny-gpu
Language: SystemVerilog
License Family: other
Last push: 2024-08-18T06:06:13+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 62
- inputs: {"age_days": 876, "days_push": 745, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12868, forks 1241 (observed 2026-08-28T04:10:59.956199+00:00)

## What it is
A minimal GPU implementation written in Verilog, designed to teach how GPUs work at the hardware level. It includes fully documented architecture and ISA docs, matrix addition/multiplication kernels, and kernel simulation with execution traces.

## Use cases
- learn how GPUs work from the ground up
- understand SIMD parallelization in hardware
- study GPU memory architecture and bandwidth constraints
- simulate simple GPU kernels with execution traces
- explore GPGPU and ML accelerator design principles

## When to choose
- you want a small, readable codebase for learning GPU hardware architecture
- you need documented Verilog examples of GPU cores, threads, and memory
- you are studying how modern hardware accelerators like TPUs work

## When to avoid
- you need a production-grade or feature-complete GPU implementation
- you want to run real graphics workloads
- you need a synthesizable design for an FPGA project without modifications

## Facets
- artifact type: learning-resource
- maturity: stable
- function: simulation, developer-tools
- domain: hardware, education, gpu-computing
- platform: cross-platform
- tags: verilog, gpu-architecture, hardware-design, simd, systemverilog, educational

## Member repositories
- adam-maj/tiny-gpu (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:59.956199+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:13:43.711393+00:00, confidence not recorded.
  - readme: https://github.com/adam-maj/tiny-gpu (fetched 2026-08-28T04:10:59.956199+00:00, sha 22cc31486010)
- Data as of 2026-08-30T08:39:29.467469+00:00.