{"id":26129,"date":"2022-01-09T15:49:10","date_gmt":"2022-01-09T13:49:10","guid":{"rendered":"https:\/\/hgpu.org\/?p=26129"},"modified":"2022-01-09T15:49:10","modified_gmt":"2022-01-09T13:49:10","slug":"cfu-playground-full-stack-open-source-framework-for-tiny-machine-learning-tinyml-acceleration-on-fpgas","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=26129","title":{"rendered":"CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs"},"content":{"rendered":"<p>We present CFU Playground, a full-stack open-source framework that enables rapid and iterative design of machine learning (ML) accelerators for embedded ML systems. Our toolchain tightly integrates open-source software, RTL generators, and FPGA tools for synthesis, place, and route. This full-stack development framework gives engineers access to explore bespoke architectures that are customized and co-optimized for embedded ML. The rapid, deploy-profile-optimization feedback loop lets ML hardware and software developers achieve significant returns out of a relatively small investment in customization. Using CFU Playground&#8217;s design loop, we show substantial speedups (55x-75x) and design space exploration between the CPU and accelerator.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present CFU Playground, a full-stack open-source framework that enables rapid and iterative design of machine learning (ML) accelerators for embedded ML systems. Our toolchain tightly integrates open-source software, RTL generators, and FPGA tools for synthesis, place, and route. This full-stack development framework gives engineers access to explore bespoke architectures that are customized and co-optimized [&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":[11,3],"tags":[1782,533,377,1025,176],"class_list":["post-26129","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-design-space-exploration","tag-fpga","tag-machine-learning","tag-package"],"views":1787,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26129","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=26129"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26129\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26129"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26129"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26129"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}