fma

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

Music genre classification model using CRNN

Recommending Music using a Convolutional Neural Network.

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

IEEE 754 standard floating point unit fpu single double precision verilog vhdl riscv

FMA Transmutation Circles

Computing FLOPs with Intel Software Development Emulator (Intel SDE)

A fast C99 vector library with Nim bindings.

IEEE 754 standard floating point unit fpu single precision verilog vhdl riscv

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

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

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

X86-64 bilateral instruction tokenizer implemented in C. Supports the following processor extensions: AES, AVX, AVX2, AVX512, FMA, MMX, SSE, SSE2, SSE3, SSE4, x87(FPU), VMX. In order to ease testing, a diassembler which transforms tokens into compilable assembly (for NASM compiler) has been implemented.

FMA convnet features

Python library to detect CPU SIMD capabilities.

Framework for making computation on CPU

SIMD accelerate xoshiro128, generate 256 bits 'number' at once