Chromium fork named after radioactive element No. 90. Windows and MacOS/Raspi/Android/Special builds are in different repositories, links are towards the top of the README.md.

Performance-portable, length-agnostic SIMD with runtime dispatch

Firefox fork with compiler optimizations and patches from Librewolf, Waterfox, and GNU IceCat.

Accelerate SHA256 computations in pure Go using AVX512, SHA Extensions for x86 and ARM64 for ARM. On AVX512 it provides an up to 8x improvement (over 3 GB/s per core). SHA Extensions give a performance boost of close to 4x over native.

Repo to serve AVX2 Windows builds of Thorium. https://github.com/Alex313031/Thorium/

flat assembler g - adaptable assembly engine

The hyper-hackable text editor - Compiler Optimized, Community Maintained Fork

AVX-512 documentation beyond what Intel provides

Information about AVX-512 support on recent Intel processors

Windows 7 builds of Mercury Browser (Based on ESR115 rather than stable tip-of-tree)

Repo to serve AVX2 Linux builds of Thorium. https://github.com/Alex313031/Thorium/

One header file library that implement missing transcendental math functions (cos, sin, acos, and more....) using 100% AVX/Neon instructions (no branching)

Document Level Sentiment Analysis is an End-to-End deep learning workflow using Hugging Face transformers API to do a "classification" task at document level, to analyze the sentiment of input document containing English sentences or paragraphs.

Implementation of 2D Convolution operation for Neural Networks using Intel x86(i368)/x86-6(amd64) AVX-256 instructions. All data flow methods, i.e input stationary, weight stationary and output stationary are implemented. The forward pass of Alexnet architecture is constructed using it.

Convolutional Neural Network Optimization using Intel AVX and OpenMP

rewrite CON_INT function to build SIMD-version convolution

Simple and performance neural network .NET library with AVX acceleration

AVX2 and SSE2 usecases and benchmarks

What features does your CPU and OS support?

an exercise in SIMD-optimization