{"id":5661,"date":"2011-09-23T11:46:40","date_gmt":"2011-09-23T08:46:40","guid":{"rendered":"http:\/\/hgpu.org\/?p=5661"},"modified":"2011-09-23T11:46:40","modified_gmt":"2011-09-23T08:46:40","slug":"acceleration-of-functional-validation-using-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5661","title":{"rendered":"Acceleration of Functional Validation Using GPGPU"},"content":{"rendered":"<p>Logic simulation of a VLSI chip is a computationally intensive process. There exists an urgent need to map functional validation algorithms onto parallel architectures to aid hardware designers in meeting time-to-market constraints. In this paper, we propose three novel methods for logic simulation of combinational circuits on GPGPUs. Initial experiments run on two methods using benchmark circuits using NVIDIA GPGPUs suggest that these methods can be used for accelerating the EDA design flow process.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Logic simulation of a VLSI chip is a computationally intensive process. There exists an urgent need to map functional validation algorithms onto parallel architectures to aid hardware designers in meeting time-to-market constraints. In this paper, we propose three novel methods for logic simulation of combinational circuits on GPGPUs. Initial experiments run on two methods using [&hellip;]<\/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":[36,11,3],"tags":[1787,451,1782,534,20,1056,202],"class_list":["post-5661","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-benchmarking","tag-computer-science","tag-electronic-design-automation","tag-nvidia","tag-nvidia-quadro-fx-1700","tag-tesla-c870"],"views":2205,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5661","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=5661"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5661\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}