Papers › Fast and Practical Strassen's Matrix Multiplication using FPGAs
Fast and Practical Strassen's Matrix Multiplication using FPGAs
Afzal Ahmad, Linfeng Du, Wei Zhang
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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(n³) for n×n matrices. Strassen's algorithm improves this to 𝒪(n^(2.807)), 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.
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