In this paper, we propose an architecture of tessellation hardware to save memory bandwidth in a mobile multimedia processor. To reduce the implementation overhead, floating-point computations of tessellation are accelerated by the conventional GPU pipeline, and only tessellation-specific control logic is handled by an additional hardware unit. Tightly coupled with a vertex shader, the additional unit dynamically produces topological configurations and parametric coordinates of refinement patterns in the type of indexed triangle strips for object-level adaptive tessellation. The topological configurations improve the efficiency of the vertex cache so as to avoid redundant shader operations. Since the proposed tessellator is area-efficient and does not require intermediate memory accesses, its architecture is especially appropriate for the mobile environment, which adopts a shared bus and unified external memory architecture. The proposed tessellator is fabricated on a chip using 0.18microm CMOS technology. With 6.2% additional hardware for a dual-core vertex shader, the implemented chip performs 120\u00a0Mvertices\/s vertex shading and saves memory bandwidth up to 250 times in tessellation.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose an architecture of tessellation hardware to save memory bandwidth in a mobile multimedia processor. To reduce the implementation overhead, floating-point computations of tessellation are accelerated by the conventional GPU pipeline, and only tessellation-specific control logic is handled by an additional hardware unit. Tightly coupled with a vertex shader, the additional […]<\/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":[11,3],"tags":[865,1782,1785,884,866],"class_list":["post-1721","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-architecture","tag-computer-science","tag-hardware","tag-memory","tag-tessellation"],"views":2815,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1721","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=1721"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1721\/revisions"}],"predecessor-version":[{"id":13568,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1721\/revisions\/13568"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1721"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1721"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1721"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}