Integrating a large number of on-chip voltage regulators holds the promise of solving many power delivery challenges through strong local load regulation and facilitates system-level power management. The quantitative understanding of such complex power delivery networks (PDNs) is hampered by the large network complexity and interactions between passive on-die\/package-level circuits and a multitude of nonlinear active regulators. We develop a fast combined GPU-CPU analysis engine encompassing several simulation strategies, optimized for various subcomponents of the network. Using accurate quantitative analysis, we demonstrate the significant performance improvement brought by on-chip low-dropout regulators (LDOs) in terms of suppressing high-frequency local voltage droops and avoiding the mid-frequency resonance caused by off-chip inductive parasitics. We perform comprehensive analysis on the tradeoffs among overhead of on-chip LDOs, maximum voltage droop and overall power efficiency. We conduct systematic design optimization by developing a simulation-based nonlinear optimization strategy that determines the optimal number of on-chip LDOs required and on-board input voltage, and the corresponding voltage droop and power efficiency for PDNs with multiple power domains.<\/p>\n","protected":false},"excerpt":{"rendered":"
Integrating a large number of on-chip voltage regulators holds the promise of solving many power delivery challenges through strong local load regulation and facilitates system-level power management. The quantitative understanding of such complex power delivery networks (PDNs) is hampered by the large network complexity and interactions between passive on-die\/package-level circuits and a multitude of nonlinear […]<\/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":[1782,14,633,342,20,311,298],"class_list":["post-5035","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-hardware-architecture","tag-nonlinear-optimization","tag-nvidia","tag-nvidia-geforce-9800-gx2","tag-optimization"],"views":2274,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5035","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=5035"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5035\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5035"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5035"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5035"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}