In this report, we analyze the computational and performance aspects of current state-of-the-art object recognition and optical flow algorithms. First, we identify important algorithms for object recognition and optical flow, then we perform a pattern decomposition to identify key computations. We include profiles of the runtime and energy efficiency (GFLOPS\/W) for our implementation of these applications on a commercial architecture. Finally, we include an analysis of memory-bandwidth boundedness for optical flow to identify opportunities for communication-avoiding algorithms. Our results were measured on an Intel i7-4770K (Haswell) reference platform. A five-layer convolutional neural network used for object classification achieves 0.70 GFLOPS\/W, which is 21% of the theoretical compute bound for this Haswell processor. On the Horn-Schunck, Lucas-Kanade, and Brox optical flow methods our implementations achieve 0.0338, 0.0103, and 0.0203 GFLOPS\/W respectively. Our implementation achieves 7.9% of the theoretical bandwidth bound, assuming no cross-iteration memory optimization, for Horn-Schunk optical flow using the Jacobi solver, and 9.7% of the bandwidth bound for the conjugate-gradient solver. To improve performance, we will focus first on increasing bandwidth utilization, then on doing cross-iteration memory optimizations such as blocking and tiling the Jacobi solver and employing communication-avoiding linear solvers. We also compare the runtime-accuracy tradeoffs for each optical flow method. We find that each method has distinct advantages over the other methods in terms of the runtime-accuracy tradeoff, so we will continue to develop and support all three methods in the future.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this report, we analyze the computational and performance aspects of current state-of-the-art object recognition and optical flow algorithms. First, we identify important algorithms for object recognition and optical flow, then we perform a pattern decomposition to identify key computations. We include profiles of the runtime and energy efficiency (GFLOPS\/W) for our implementation of these […]<\/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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,90,3],"tags":[1787,7,1550,1782,34,1793,896],"class_list":["post-11817","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-ati","tag-ati-radeon-hd-7990","tag-computer-science","tag-neural-networks","tag-opencl","tag-optical-flow"],"views":2392,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11817","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=11817"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11817\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11817"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11817"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11817"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}