# liangkangnan/tinyriscv

A very simple and easy to understand RISC-V core.

Repository: https://github.com/liangkangnan/tinyriscv
Canonical: https://ross.abutalabs.com/products/tinyriscv
Language: C
License: Apache-2.0
License Family: permissive
Last push: 2023-11-09T12:47:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2459, "days_push": 1028, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1510, forks 241 (observed 2026-08-28T04:04:55.792309+00:00)

## What it is
A simple, easy-to-understand 32-bit single-core RISC-V processor core written in Verilog, targeting ARM Cortex-M3 class performance. It implements RV32IM with a three-stage pipeline and includes simulation, test, and FPGA tooling for learning how a CPU works.

## Use cases
- learn how a RISC-V CPU is designed from scratch
- study a simple pipelined processor implementation in Verilog
- run RISC-V instruction set compatibility tests
- synthesize a small RISC-V core onto an FPGA
- run C programs and FreeRTOS on a homemade RISC-V core
- teach computer architecture with a readable CPU codebase

## When to choose
- you are a beginner wanting a readable RISC-V core to learn from
- you need a small RV32IM core for FPGA experimentation or coursework
- you want a reference implementation paired with design documentation

## When to avoid
- you need a high-performance or production-grade RISC-V core
- you require RV64, advanced extensions, or out-of-order execution
- you need formal verification or commercial support

## Facets
- artifact type: learning-resource
- maturity: active
- function: simulation, embedded
- domain: hardware, education, embedded-systems
- platform: windows, cross-platform
- tags: risc-v, verilog, fpga, processor-core, hardware-design, open-source-hardware, educational, linux

## Member repositories
- liangkangnan/tinyriscv (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:55.792309+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 2026-08-30T04:32:26.587017+00:00, confidence not recorded.
  - readme: https://github.com/liangkangnan/tinyriscv (fetched 2026-08-28T04:04:55.792309+00:00, sha b5aedc215119)
- Data as of 2026-08-30T08:39:29.467469+00:00.