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Archit. Code Optim."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Deep Neural Networks (DNNs) have achieved great progress in academia and industry. But they have become computational and memory intensive with the increase of network depth. Previous designs seek breakthroughs in software and hardware levels to mitigate these challenges. At the software level, neural network compression techniques have effectively reduced network scale and energy consumption. However, the conventional compression algorithm is complex and energy intensive. At the hardware level, the improvements in the semiconductor process have effectively reduced power and energy consumption. However, it is difficult for the traditional Von-Neumann architecture to further reduce the power consumption, due to the memory wall and the end of Moore\u2019s law. To overcome these challenges, the spintronic device based DNN machines have emerged for their non-volatility, ultra low power, and high energy efficiency. However, there is no spin-based design that has achieved innovation at both the software and hardware level. Specifically, there is no systematic study of spin-based DNN architecture to deploy compressed networks.<\/jats:p>\n          <jats:p>\n            In our study, we present an ultra-efficient Spin-based Architecture for Compressed DNNs (SAC), to substantially reduce power consumption and energy consumption. Specifically, we propose a One-Step Compression algorithm (OSC) to reduce the computational complexity with minimum accuracy loss. We also propose a spin-based architecture to realize better performance for the compressed network. Furthermore, we introduce a novel computation flow that enables the reuse of activations and weights. Experimental results show that our study can reduce the computational complexity of compression algorithm from \ud835\udcaa(\n            <jats:italic>Tk<\/jats:italic>\n            <jats:sup>3<\/jats:sup>\n            to \ud835\udcaa(\n            <jats:italic>k<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            log\n            <jats:italic>k<\/jats:italic>\n            ), and achieve 14\u00d7 \u223c 40\u00d7 compression ratio. Furthermore, our design can attain a 2\u00d7 enhancement in power efficiency and a 5\u00d7 improvement in computational efficiency compared to the Eyeriss. Our models are available at an anonymous link\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/bit.ly\/39cdtTa\">https:\/\/bit.ly\/39cdtTa<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3632957","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T11:54:16Z","timestamp":1699962856000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["SAC: An Ultra-Efficient Spin-based Architecture for Compressed DNNs"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5600-3740","authenticated-orcid":false,"given":"Yunping","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Microelectronics and Microprocessors, School of Computy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-4060","authenticated-orcid":false,"given":"Sheng","family":"Ma","sequence":"additional","affiliation":[{"name":"The Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4912-0364","authenticated-orcid":false,"given":"Heng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7878-3998","authenticated-orcid":false,"given":"Libo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9929-6459","authenticated-orcid":false,"given":"Yi","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi. 2016. 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