Integrated circuit (IC) design automation has traditionally followed a hierarchical approach. Modern IC design flow is divided into sequentially-addressed design and optimization layers; each successively finer in design detail and data granularity while increasing in computational complexity. Eventual agreement across the design layers signals design closure. Obtaining design closure is a continual problem, as lack of awareness and interaction between layers often results in multiple design flow iterations. In this work, we propose parallel cross-layer optimization, in which the boundaries between design layers are broken, allowing for a more informed and efficient exploration of the design space. We leverage the heterogeneous parallel computational power in current and upcoming multi-core\/many-core computation platforms to suite the heterogeneous characteristics of multiple design layers. Specifically, we unify the highlevel and physical synthesis design layers for parallel cross-layer IC design optimization. In addition, we introduce a massively-parallel GPU floorplanner with local and global convergence test as the proposed physical synthesis design layer. Our results show average performance gains of 11X speed-up over state-of-the-art.<\/p>\n","protected":false},"excerpt":{"rendered":"
Integrated circuit (IC) design automation has traditionally followed a hierarchical approach. Modern IC design flow is divided into sequentially-addressed design and optimization layers; each successively finer in design detail and data granularity while increasing in computational complexity. Eventual agreement across the design layers signals design closure. Obtaining design closure is a continual problem, as lack […]<\/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":[11,89,3],"tags":[596,1782,14,534,20,199],"class_list":["post-4358","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-cad","tag-computer-science","tag-cuda","tag-electronic-design-automation","tag-nvidia","tag-tesla-c1060"],"views":1896,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4358","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=4358"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4358\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4358"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4358"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4358"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}