{"id":19222,"date":"2019-12-08T19:48:54","date_gmt":"2019-12-08T17:48:54","guid":{"rendered":"https:\/\/hgpu.org\/?p=19222"},"modified":"2019-12-08T19:48:54","modified_gmt":"2019-12-08T17:48:54","slug":"the-bondmachine-toolkit-enabling-machine-learning-on-fpga","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=19222","title":{"rendered":"The BondMachine toolkit: Enabling Machine Learning on FPGA"},"content":{"rendered":"<p>The BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In the present paper we will provide a technical overview of the key aspects of the BondMachine toolkit, highlighting the advancements brought about by the porting of Go code in hardware. We will then show a cloud-based BM as a Service deployment. Finally, we will focus on TensorFlow, and in this context we will show how we plan to benchmark the system with a ML tracking reconstruction from pp collision at the LHC.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In the present paper we will provide a technical overview of the key aspects of [&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":[1782,377,2042,1025],"class_list":["post-19222","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-fpga","tag-go","tag-machine-learning"],"views":1865,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19222","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=19222"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19222\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=19222"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=19222"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=19222"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}