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Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>In the past years, several works have proposed custom hardware and software-based techniques for the acceleration of Convolutional Neural Networks (CNNs). Most of these works focus on saving computations by changing the used precision or modifying frame processing. To reach a more aggressive energy reduction, in this paper we propose software-only modifications to the CNNs inference process.<\/jats:p>\n          <jats:p>Our approach exploits the inherent locality in videos by replacing entire frame computations with a movement prediction algorithm. Furthermore, when a frame must be processed, we avoid energy-demanding floating-point operations, and at the same time reduce memory accesses by employing look-up tables in place of the original convolutions.<\/jats:p>\n          <jats:p>Using the proposed approach, one can reach significant energy gains of more than 25\u00d7 for security cameras, and 12\u00d7 for moving vehicles applications, with only small software modifications.<\/jats:p>","DOI":"10.1145\/3358174","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T13:13:05Z","timestamp":1570713185000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Aggressive Energy Reduction for Video Inference with Software-only Strategies"],"prefix":"10.1145","volume":"18","author":[{"given":"Larissa Rozales","family":"Gon\u00e7alves","sequence":"first","affiliation":[{"name":"Federal University of Rio Grande do Sul - Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael F\u00e3o De","family":"Moura","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do Sul - Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Carro","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do Sul - Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,10,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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