Year: 2024
Type: conference-paper
Abstract: Matrix multiplication is a cornerstone operation in a wide array of scientific fields, including machine learning and computer graphics. The standard algorithm for matrix multiplication has a complexity of $O\left(n^{3}\right)$ for $n \times n$ matrices. Strassen’s algorithm improves this to $O\left(n^{2.807}\right)$, but its practicality is limited for small to medium matrix sizes due to the large number of additions it introduces. This paper presents a novel FPGA-based implementation of Strassen’s algorithm that achieves superior speed over an optimized General Matrix Multiply (GeMM) implementation for matrices as small as n = 256. Our design, tested extensively on two high-performance FPGA accelerators (Alveo U50 and U280) across various data types, matches or surpasses the performance of a highly optimized baseline across a range of matrix sizes.
Authors: Afzal Ahmad, Linfeng Du, Wei Zhang
Institution: Hong Kong University of Science and Technology
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403278915