{"id":1085,"date":"2010-11-02T11:30:07","date_gmt":"2010-11-02T11:30:07","guid":{"rendered":"http:\/\/hgpu.org\/?p=1085"},"modified":"2010-11-02T11:30:07","modified_gmt":"2010-11-02T11:30:07","slug":"gpu-powered-cnn-simulator-simcnn-with-graphical-flow-based-programmability","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1085","title":{"rendered":"GPU powered CNN simulator (SIMCNN) with graphical flow based programmability"},"content":{"rendered":"<p>In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a simple PC environment cheaply. Therefore, analysis of CNN dynamics become more feasible with a common hardware setup. Our measurements demonstrate the efficiency of the realized system. In the case of simpler algorithms, real-time execution is also possible.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a [&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":[11,3],"tags":[329,330,1782,34],"class_list":["post-1085","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-cellular-neural-networks","tag-cnn","tag-computer-science","tag-neural-networks"],"views":2892,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1085","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=1085"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1085\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1085"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1085"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1085"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}