{"id":8815,"date":"2013-01-18T23:46:27","date_gmt":"2013-01-18T21:46:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=8815"},"modified":"2013-01-18T23:46:27","modified_gmt":"2013-01-18T21:46:27","slug":"scan-test-power-simulation-on-gpgpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8815","title":{"rendered":"Scan Test Power Simulation on GPGPUs"},"content":{"rendered":"<p>The precise estimation of dynamic power consumption, power droop and temperature development during scan test require a very large number of time-aware gate-level logic simulations. Until now, such characterizations have been feasible only for rather small designs or with reduced precision due to the high computational demands. We propose a new, throughput-optimized timing simulator on running on GPGPUs to accelerate these tasks by more than two orders of magnitude and thus providing for the first time precise and comprehensive toggle data for industrial-sized designs and over long scan test operations. Hazards and pulse-filtering are supported for the first time in a GPGPU accelerated simulator, and the system can easily be extended to even more sophisticated delay and power models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The precise estimation of dynamic power consumption, power droop and temperature development during scan test require a very large number of time-aware gate-level logic simulations. Until now, such characterizations have been feasible only for rather small designs or with reduced precision due to the high computational demands. We propose a new, throughput-optimized timing simulator on [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,344,841,20],"class_list":["post-8815","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-energy-efficient-computing","tag-filtering","tag-nvidia"],"views":2014,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8815","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=8815"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8815\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}