# Fast and Practical Strassen's Matrix Multiplication using FPGAs

arXiv: https://arxiv.org/abs/2406.02088 · HTML: https://mlsanity.com/paper/2406.02088/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Changing Base Without Losing Pace: A GPU-Efficient Alternative to MatMul in DNNs](https://mlsanity.com/paper/2503.12211/) (2025)
- [Exact Causal Attention with 10% Fewer Operations](https://mlsanity.com/paper/2510.05175/) (2025)

## Abstract (arXiv)

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(n2.807), 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.