Home Projects Simd
Simd
C++

Simd

C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE for ARM, HVX for Hexagon

by ermig1979 · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
amxarmavxMITC++
View on GitHub
In plain words

Process images and implement machine learning algorithms using optimized code for various processors.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
Simd — GitHub preview card
📈 Star history
2 2592 257
2026-07-072026-08-31
📈 Track Simd

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE for ARM, HVX for Hexagon

Simd has 2.3k stars on GitHub. It has been forked 455 times. Simd is written mainly in C++. It has been in active development since 2015. Simd is available under the MIT license. Its main topics are amx, arm, avx, avx512.

Frequently asked questions

What is Simd?

C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE for ARM, HVX for Hexagon

Is Simd open source?

Simd is an open-source project. It is released under the MIT license.

Is Simd free?

Yes. Simd is free and open source — you can use, modify and self-host it.

What license does Simd use?

Simd is available under the MIT license.

What language is Simd written in?

Simd is written mainly in C++.

🏅 Maintainer of this project?
olud.ai badge — Simd

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=ermig1979-simd)](https://olud.ai/project/ermig1979-simd.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.