{"id":29241,"date":"2024-06-09T14:11:37","date_gmt":"2024-06-09T11:11:37","guid":{"rendered":"https:\/\/hgpu.org\/?p=29241"},"modified":"2024-06-09T14:11:37","modified_gmt":"2024-06-09T11:11:37","slug":"fast-and-practical-strassens-matrix-multiplication-using-fpgas","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=29241","title":{"rendered":"Fast and Practical Strassen&#8217;s Matrix Multiplication using FPGAs"},"content":{"rendered":"<p>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\u00d7n matrices. Strassen&#8217;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&#8217;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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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\u00d7n matrices. Strassen&#8217;s algorithm improves this to O(n2.807), but its practicality is limited for small to medium matrix sizes due to the large number [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,90,3],"tags":[430,1782,377,1419,37,1025,324,1793,176],"class_list":["post-29241","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-blas","tag-computer-science","tag-fpga","tag-gemm","tag-linear-algebra","tag-machine-learning","tag-matrix-multiplication","tag-opencl","tag-package"],"views":2182,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=29241"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29241\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}