# sipeed/TinyMaix

TinyMaix is a tiny inference library for microcontrollers (TinyML).

Repository: https://github.com/sipeed/TinyMaix
Canonical: https://ross.abutalabs.com/products/tinymaix
Language: C
License: Apache-2.0
License Family: permissive
Last push: 2025-02-05T01:57:29+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 5, release rhythm 35, longevity 100
- inputs: {"age_days": 1486, "days_push": 575, "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 1071, forks 169 (observed 2026-08-28T04:03:28.234656+00:00)

## What it is
TinyMaix is a tiny neural network inference library for microcontrollers (TinyML), with core code under 400 lines and a .text section under 3KB. It supports INT8/FP32/FP16 (and experimental FP8) models converted from Keras h5 or TFLite, with SIMD acceleration for ARM, RISC-V, CSKY, and x86.

## Use cases
- run mnist inference on an Arduino ATmega328
- deploy neural network models on resource-limited microcontrollers
- run tinyml models on embedded chips with only 2KB of RAM
- convert keras or tflite models for mcu inference
- accelerate inference with ARM SIMD/NEON or RISC-V vector extensions
- train and deploy models online via MaixHub

## When to choose
- you need neural network inference on very small MCUs with limited flash and RAM
- you want a simple, readable TinyML library you can understand in 30 minutes
- you need multi-architecture support including ARM, RISC-V, and x86
- you want full static memory configuration

## When to avoid
- you need a feature-rich production TinyML stack with CMSIS-NN optimizations
- you need large or complex models beyond tinyml scale
- you need many advanced ops and new model architectures
- you want a mature ecosystem like TFLite Micro or microTVM

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-inference, embedded
- domain: machine-learning, embedded-systems, artificial-intelligence
- platform: embedded, c, cross-platform, iot
- tags: tinyml, neural-network-inference, microcontroller, int8-quantization, mcu

## Member repositories
- sipeed/TinyMaix (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.234656+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 2026-08-30T06:53:52.643063+00:00, confidence not recorded.
  - readme: https://github.com/sipeed/TinyMaix (fetched 2026-08-28T04:03:28.234656+00:00, sha a3322e979776)
- Data as of 2026-08-30T08:39:29.467469+00:00.