{"id":268833,"date":"2016-08-01T06:56:40","date_gmt":"2016-08-01T13:56:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=268833"},"modified":"2017-06-13T13:21:49","modified_gmt":"2017-06-13T20:21:49","slug":"project-fdnn-fpga-based-deep-neural-networks","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-fdnn-fpga-based-deep-neural-networks\/","title":{"rendered":"Project FDNN: FPGA-based Deep Neural Networks"},"content":{"rendered":"<p><span lang=\"EN-US\">This project aims to accelerate the inference and training of Deep Neural Networks (DNN) using\u00a0FPGAs for high energy efficiency and low latency in data centers.<\/span><\/p>\n<p><span lang=\"EN-US\">\u00a0<\/span><span lang=\"EN-US\">We have been developing a CNN (Convolutional Neural Network) accelerator based on an embedded FPGA platform. A dynamic-precision data quantization method and a convolver design that is ef\ufb01cient for all layer types in CNN are proposed to improve the bandwidth and resource utilization. Results show that only 0.4% accuracy loss is introduced by our data quantization \ufb02ow for the very deep VGG16 model when 8\/4-bit quantization is used. VGG16-SVD is implemented on an embedded FPGA platform (Xilinx Zynq) as a case study. The system on Xilinx Zynq ZC706 board achieves a framerate at 4.45 fps with the top-5 accuracy of 86.66% using 16-bit quantization. The average performance of Convolutional layers and the full CNN is 187.8GOP\/s and 137.0GOP\/s under 150MHz working frequency<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This project aims to accelerate the inference and training of Deep Neural Networks (DNN) using\u00a0FPGAs for high energy efficiency and low latency in data centers. \u00a0We have been developing a CNN (Convolutional Neural Network) accelerator based on an embedded FPGA platform. A dynamic-precision data quantization method and a convolver design that is ef\ufb01cient for all [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13562,13547],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-268833","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-systems-and-networking","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2015-02-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/268833","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/268833\/revisions"}],"predecessor-version":[{"id":390299,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/268833\/revisions\/390299"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=268833"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=268833"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=268833"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=268833"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=268833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}