Implementations of SIMD instruction sets for systems which don't natively support them.

Performance-optimized wheels for TensorFlow (SSE, AVX, FMA, XLA, MPI)

Boost SIMD

Recommending Music using a Convolutional Neural Network.

Music genre classification model using CRNN

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

FMA Transmutation Circles

Computing FLOPs with Intel Software Development Emulator (Intel SDE)

Data pipeline and training pipeline for 🎵 music genre classification from FMA dataset

A collection of highly optimized, SIMD-accelerated (SSE, AVX, FMA, NEON) functions written in C

software implementation of Fused-Multiply Add for 64-bit floats

Fast avx2/fma3 dgemm and sgemm subroutines for medium to large matrices(>2000*2000) on haswell/skylake/zen processors, with performances comparable to MKL.

Python library to detect CPU SIMD capabilities.

FMA convnet features

Fused Multiply Add (FMA) operation on Altera DE1-SoC Cyclone V development board.

Framework for making computation on CPU

Benchmarks on SIMD instructions : SSE, AVX, AVX512

Performed music data visualization using python-seaborn library and matplotlib library

offline artistic image generator

Application de classification par apprentissage profond pour mon projet d'intégration au Collège de Bois-de-Boulogne à la session d'hiver 2020