# google/gemmlowp

Low-precision matrix multiplication

Repository: https://github.com/google/gemmlowp
Canonical: https://ross.abutalabs.com/products/gemmlowp
Language: C++
License: Apache-2.0
License Family: permissive
Last push: 2024-01-29T14:40:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4076, "days_push": 947, "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 1843, forks 462 (observed 2026-08-28T04:05:43.259439+00:00)

## What it is
gemmlowp is a small, self-contained, headers-only C++ library for low-precision (quantized) general matrix multiplication (GEMM). It provides optimized code paths for ARM NEON and x86 SSE4.1 and is widely used as a quantized inference kernel in machine-learning runtimes like TensorFlow Lite.

## Use cases
- perform quantized 8-bit matrix multiplication on mobile CPUs
- accelerate low-precision GEMM kernels for neural network inference
- embed a lightweight matrix multiply library in an embedded ARM project
- speed up integer matrix multiplication with NEON or SSE4.1 intrinsics
- implement quantized convolution layers for on-device ML
- profile and benchmark low-precision GEMM performance

## When to choose
- you need fast quantized/low-precision GEMM on ARM or x86 with minimal dependencies
- you want a header-only library that is easy to embed in C++11 projects
- you are building or extending a mobile or embedded ML inference engine

## When to avoid
- you need a full linear algebra library (BLAS, solvers, decompositions)
- you need floating-point GEMM with broad GPU acceleration
- you require frequent updates or official vendor support, as the project is in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, math
- domain: machine-learning, deep-learning, performance
- platform: cpp, windows, embedded, cross-platform
- tags: gemm, low-precision, quantization, header-only, matrix-multiplication, neon, simd, inference, algorithms, linux, macos, android

## Member repositories
- google/gemmlowp (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:43.259439+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 2026-08-30T03:17:57.752895+00:00, confidence not recorded.
  - readme: https://github.com/google/gemmlowp (fetched 2026-08-28T04:05:43.259439+00:00, sha 5ebfb80a4714)
- Data as of 2026-08-30T08:39:29.467469+00:00.