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Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. This article presents the design and evaluation of an accelerator for CoNNs. The system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly layers to reduce computational overheads typically associated with a CoNN, (iii) a mixed-signal CeNN architecture that performs CoNN computations in the analog and mixed signal domain, and (iv) design space exploration that identifies what CeNN-based algorithm and architectural features fare best compared to existing algorithms and architectures when evaluated over common datasets\u2014MNIST and CIFAR-10. Notably, the proposed approach can lead to 8.7\u00d7 improvements in energy-delay product (EDP) per digit classification for the MNIST dataset at iso-accuracy when compared with the state-of-the-art DNN engine, while our approach could offer 4.3\u00d7 improvements in EDP when compared to other network implementations for the CIFAR-10 dataset.<\/jats:p>","DOI":"10.1145\/3304110","type":"journal-article","created":{"date-parts":[[2019,3,28]],"date-time":"2019-03-28T12:23:24Z","timestamp":1553775804000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["A Mixed Signal Architecture for Convolutional Neural Networks"],"prefix":"10.1145","volume":"15","author":[{"given":"Qiuwen","family":"Lou","sequence":"first","affiliation":[{"name":"University of Notre Dame, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyun","family":"Pan","sequence":"additional","affiliation":[{"name":"University of Kensas, Kensas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"McGuinness","sequence":"additional","affiliation":[{"name":"University of Notre Dame, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andras","family":"Horvath","sequence":"additional","affiliation":[{"name":"Pazmany Peter Catholic University, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azad","family":"Naeemi","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Niemier","sequence":"additional","affiliation":[{"name":"University of Notre Dame, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"X. 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