GitHub

harshithparitala/systolic_array-3x3-matrix

Comparison of Matrix Multiplication in Traditional vs. Systolic Architectures In a traditional computing architecture (such as CPUs or GPUs), matrix multiplication is performed by fetching data from ...
IEEE

A Flexible-blocking Based Approach for Performance Tuning of Matrix Multiplication Routines for Large Matrices with Edge Cases

Abstract: Efficient and scalable matrix operations are being highly demanding in the recent era of Machine Learning, Deep Learning, and Big Data Analytics. The two commonly used matrix-matrix ...
insideHPC

Intel MKL Speeds Up Small Matrix-Matrix Multiplication for Automatic Driving

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...
IEEE

VLSI Implementation of Pipelined PE Systolic Array-Based 3x3 Matrix Multiplication for Deep Neural Network Accelerator

Abstract: The demand for efficient, low-power, and high-speed deep neural network (DNN) accelerators has driven the need for specialized hardware architectures. This work presents the VLSI ...
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 ...
Ars Technica

Researchers upend AI status quo by eliminating matrix multiplication in LLMs

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...