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However, the high accuracy usually comes at the cost of substantial computation and energy consumption, making it difficult to be deployed on mobile and embedded devices. In CNNs, the compute-intensive convolutional layers are usually followed by a ReLU activation layer, which clamps negative outputs to zeros, resulting in large activation sparsity. By exploiting such sparsity in CNN models, we propose a software-hardware co-design BitSET, that aggressively saves energy during CNN inference. The bit-serial BitSET accelerator adopts a prediction-based bit-level early termination technique that terminates the ineffectual computation of negative outputs early. To assist the algorithm, we propose a novel weight encoding that allows more accurate predictions with fewer bits. BitSET leverages the bit-level computation reduction both in the predictive early termination algorithm and in the non-predictive, energy-efficient bit-serial architecture. Compared to UNPU, an energy-efficient bit-serial CNN accelerator, BitSET yields an average 1.5\u00d7 speedup and 1.4\u00d7 energy efficiency improvement with no accuracy loss due to a 48% reduction in bit-level computations. Relaxing the allowed accuracy loss to 1% increases the gains to an average of 1.6\u00d7 speedup and 1.4\u00d7 energy efficiency improvement.<\/jats:p>","DOI":"10.1145\/3609093","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T13:33:18Z","timestamp":1694266398000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["BitSET: Bit-Serial Early Termination for Computation Reduction in Convolutional Neural Networks"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9351-431X","authenticated-orcid":false,"given":"Yunjie","family":"Pan","sequence":"first","affiliation":[{"name":"University of Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2085-0312","authenticated-orcid":false,"given":"Jiecao","family":"Yu","sequence":"additional","affiliation":[{"name":"Facebook Inc., USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1167-3999","authenticated-orcid":false,"given":"Andrew","family":"Lukefahr","sequence":"additional","affiliation":[{"name":"Indiana University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5894-8342","authenticated-orcid":false,"given":"Reetuparna","family":"Das","sequence":"additional","affiliation":[{"name":"University of Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0438-0616","authenticated-orcid":false,"given":"Scott","family":"Mahlke","sequence":"additional","affiliation":[{"name":"University of Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"662","volume-title":"ISCA\u201918","author":"Akhlaghi Vahideh","year":"2018","unstructured":"Vahideh Akhlaghi, Amir Yazdanbakhsh, Kambiz Samadi, Rajesh K. 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