Semantic text analysis is a technique used in advertisement placement, cognitive databases and search engines. With increasing amounts of data and stringent response-time requirements, improving the underlying implementation of semantic analysis becomes critical. To this end, we look at Supervised Semantic Indexing (SSI), a recently proposed algorithm for semantic analysis. SSI ranks a large number of documents based on their semantic similarity to a text query. For each query, it computes millions of dot products on unstructured data, generates a large intermediate result, and then performs ranking. SSI underperforms on both state-of-the-art multi-cores as well as GPUs. Its performance scalability on multi-cores is hampered by their limited support for fine-grained data parallelism. GPUs, though beat multi-cores by running thousands of threads, cannot handle large intermediate data because of their small on-chip memory. Motivated by this, we present an FPGA-based hardware accelerator for semantic analysis. As a key feature, the accelerator combines hundreds of simple processing elements together with in-memory processing to simultaneously generate and process (consume) the large intermediate data. It also supports "dynamic parallelism" – a feature that configures the PEs differently for full utilization of the available processin logic after the FPGA is programmed. Our FPGA prototype is 10-13x faster than a 2.5 GHz quad-core Xeon, and 1.5-5x faster than a 240 core 1.3 GHz Tesla GPU, despite operating at a modest frequency of 125 MHz.<\/p>\n","protected":false},"excerpt":{"rendered":"
Semantic text analysis is a technique used in advertisement placement, cognitive databases and search engines. With increasing amounts of data and stringent response-time requirements, improving the underlying implementation of semantic analysis becomes critical. To this end, we look at Supervised Semantic Indexing (SSI), a recently proposed algorithm for semantic analysis. SSI ranks a large number […]<\/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":[36,11,89,3],"tags":[1787,1782,14,263,667,377,20,450,199],"class_list":["post-4931","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-data-parallelism","tag-databases","tag-fpga","tag-nvidia","tag-semantic-indexing","tag-tesla-c1060"],"views":2586,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4931","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=4931"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4931\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4931"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4931"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}