{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T16:44:05Z","timestamp":1761324245133,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"5s","license":[{"start":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T00:00:00Z","timestamp":1570492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>PIM (processing-in-memory) based hardware accelerators have shown great potentials in addressing the computation and memory access intensity of modern CNNs (convolutional neural networks). While adopting NVM (non-volatile memory) helps to further mitigate the storage and energy consumption overhead, adopting quantization, e.g., shift-based quantization, helps to tradeoff the computation overhead and the accuracy loss, integrating both NVM and quantization in hardware accelerators leads to sub-optimal acceleration.<\/jats:p>\n          <jats:p>\n            In this paper, we exploit the natural shift property of DWM (domain wall memory) to devise DWMAcc, a DWM-based accelerator with asymmetrical storage of weight and input data, to speed up the inference phase of shift-based CNNs. DWMAcc supports flexible shift operations to enable fast processing with low performance and area overhead. We then optimize it with\n            <jats:italic>zero-sharing<\/jats:italic>\n            ,\n            <jats:italic>input-reuse<\/jats:italic>\n            , and\n            <jats:italic>weight-share<\/jats:italic>\n            schemes. Our experimental results show that, on average, DWMAcc achieves 16.6\u00d7 performance improvement and 85.6\u00d7 energy consumption reduction over a state-of-the-art SRAM based design.\n          <\/jats:p>","DOI":"10.1145\/3358199","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T13:13:05Z","timestamp":1570713185000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DWMAcc"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9607-7368","authenticated-orcid":false,"given":"Zhengguo","family":"Chen","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Deng","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nong","family":"Xiao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirk","family":"Pruhs","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youtao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,10,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2016. 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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)."}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3358199","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3358199","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:32:58Z","timestamp":1750199578000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3358199"}},"subtitle":["Accelerating Shift-based CNNs with Domain Wall Memories"],"short-title":[],"issued":{"date-parts":[[2019,10,8]]},"references-count":28,"journal-issue":{"issue":"5s","published-print":{"date-parts":[[2019,10,31]]}},"alternative-id":["10.1145\/3358199"],"URL":"https:\/\/doi.org\/10.1145\/3358199","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"type":"print","value":"1539-9087"},{"type":"electronic","value":"1558-3465"}],"subject":[],"published":{"date-parts":[[2019,10,8]]},"assertion":[{"value":"2019-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-10-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}