{"id":624,"date":"2020-01-24T12:42:00","date_gmt":"2020-01-24T12:42:00","guid":{"rendered":"https:\/\/v2.joaomiguelvieira.com\/~joaovieira\/site\/?p=624"},"modified":"2022-11-02T10:30:53","modified_gmt":"2022-11-02T10:30:53","slug":"got-a-paper-accepted-in-icassp-2020","status":"publish","type":"post","link":"https:\/\/v2.joaomiguelvieira.com\/~joaovieira\/site\/2020\/01\/24\/got-a-paper-accepted-in-icassp-2020\/","title":{"rendered":"Got a paper accepted in ICASSP 2020"},"content":{"rendered":"\n

My paper titled \u201cProcessing Convolutional Neural Networks on Cache\u201d<\/a><\/em> was accepted in ICASSP 2020 to be held in Barcelona, Spain, from May 4th to May 8th.<\/p>\n\n\n\n

Abstract:<\/strong><\/p>\n\n\n\n

With the advent of Big Data application domains, several Machine Learning signal-processing algorithms, such as Convolutional Neural Networks, are required to process progressively larger datasets at a great cost in terms of both compute power and memory bandwidth. Although dedicated accelerators have been developed targeting this issue, they usually require moving massive amounts of data across the memory hierarchy to the processing cores and low-level knowledge of how data is stored in the memory devices to enable in-\/near-memory processing solutions. In this paper, we propose and assess a novel mechanism that operates at cache level, leveraging both data-proximity and parallel processing capabilities, enabled by dedicated fully-digital vector Functional Units. We also demonstrate the integration of this mechanism in a conventional CPU. The obtained results show that our engine provides performance improvements on CNNs ranging from 3.92\u00d7 to 16.6\u00d7.<\/p>\n","protected":false},"excerpt":{"rendered":"

My paper titled \u201cProcessing Convolutional Neural Networks on Cache\u201d was accepted in ICASSP 2020 to be held in Barcelona, Spain, from May 4th to May 8th. Abstract: With the advent of Big Data application domains, several Machine Learning signal-processing algorithms, such as Convolutional Neural Networks, are required to process progressively […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[151],"tags":[],"class_list":["post-624","post","type-post","status-publish","format-standard","hentry","category-research"],"yoast_head":"\nGot a paper accepted in ICASSP 2020 - Jo\u00e3o Vieira<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/v2.joaomiguelvieira.com\/~joaovieira\/site\/2020\/01\/24\/got-a-paper-accepted-in-icassp-2020\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Got a paper accepted in ICASSP 2020 - Jo\u00e3o Vieira\" \/>\n<meta property=\"og:description\" content=\"My paper titled \u201cProcessing Convolutional Neural Networks on Cache\u201d was accepted in ICASSP 2020 to be held in Barcelona, Spain, from May 4th to May 8th. 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