{"id":2211,"date":"2010-12-25T21:38:11","date_gmt":"2010-12-25T21:38:11","guid":{"rendered":"http:\/\/hgpu.org\/?p=2211"},"modified":"2010-12-25T21:38:11","modified_gmt":"2010-12-25T21:38:11","slug":"fast-acoustic-computations-using-graphics-processors","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2211","title":{"rendered":"Fast acoustic computations using graphics processors"},"content":{"rendered":"<p>In this paper we present a fast method for computing acoustic likelihoods that makes use of a graphics processing unit (GPU). After enabling the GPU acceleration the main processor runtime dedicated to acoustic scoring tasks is reduced from the largest consumer to just a few percent even when using mixture models with a large number of Gaussian components. The results show a large reduction in decoding time with no change in accuracy and we also show by using a 16bit half precision floating point format for the acoustic model parameters, storage requirements can be halved with no reduction in accuracy.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present a fast method for computing acoustic likelihoods that makes use of a graphics processing unit (GPU). After enabling the GPU acceleration the main processor runtime dedicated to acoustic scoring tasks is reduced from the largest consumer to just a few percent even when using mixture models with a large number [&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":[89,3,41],"tags":[14,20,183,1789,848],"class_list":["post-2211","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-signal-processing","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-signal-processing","tag-speech-recognition"],"views":1925,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2211","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=2211"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2211\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2211"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2211"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}