Support Vector Machines (SVMs) are a powerful supervised learning tool, providing state-of-the-art accuracy at a cost of high computational complexity. The SVM classification suffers from linear dependencies on the number of the Support Vectors and the problem’s dimensionality. In this work, we propose a scalable FPGA architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. Furthermore, this work introduces the first FPGA-oriented cascade SVM classifier scheme, which intensifies the custom-arithmetic properties of the heterogeneous architecture and boosts the classification performance even more. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2-3 orders of magnitude, compared to the CPU implementation, while outperforming other proposed FPGA and GPU approaches by more than 7 times.<\/p>\n","protected":false},"excerpt":{"rendered":"
Support Vector Machines (SVMs) are a powerful supervised learning tool, providing state-of-the-art accuracy at a cost of high computational complexity. The SVM classification suffers from linear dependencies on the number of the Support Vectors and the problem’s dimensionality. In this work, we propose a scalable FPGA architecture for the acceleration of SVM classification, which exploits […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[659,1782,377,452],"class_list":["post-5105","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computational-complexity","tag-computer-science","tag-fpga","tag-heterogeneous-systems"],"views":2451,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5105","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=5105"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5105\/revisions"}],"predecessor-version":[{"id":5206,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5105\/revisions\/5206"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}