GitHub

A Python demonstration of speeding up matrix multiplication and reducing memory using a quantum-inspired low-rank SVD trick, with examples in AI.

This repository demonstrates a powerful, classical linear algebra technique—low-rank approximation via Singular Value Decomposition (SVD)—to dramatically accelerate common matrix operations like GEMM ...
IEEE

Inverse-Designed Photonic Computing Core for Parallel Matrix-Vector Multiplication

Abstract: On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and ...
GitHub

Systolic Array Based Matrix-Vector Multiplication

This repository contains the SystemVerilog code and simulation results for a systolic array-based matrix-vector multiplication for signed 8 bit integers. The architecture is designed to perform the ...
IEEE

Exploring the Design Space of Distributed Parallel Sparse Matrix-Multiple Vector Multiplication

Abstract: We consider the distributed memory parallel multiplication of a sparse matrix by a dense matrix (SpMM). The dense matrix is often a collection of dense vectors. Standard implementations will ...
Physics World

Photonic metastructure does vector–matrix multiplication

Photonic innovation: researchers in the US have created an optical metamaterial that can perform vector–matrix multiplication. (Courtesy: iStock/Henrik5000) A new silicon photonics platform that can ...
InfoQ

MIT Researchers Open-Source Approximate Matrix Multiplication Algorithm MADDNESS

Researchers at MIT's Computer Science & Artificial Intelligence Lab (CSAIL) have open-sourced Multiply-ADDitioN-lESS (MADDNESS), an algorithm that speeds up machine learning using approximate matrix ...