{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:35:22Z","timestamp":1773246922201,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"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":[],"published-print":{"date-parts":[[2022,10,30]]},"DOI":"10.1145\/3508352.3549418","type":"proceedings-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T12:10:54Z","timestamp":1671711054000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Squeezing Accumulators in Binary Neural Networks for Extremely Resource-Constrained Applications"],"prefix":"10.1145","author":[{"given":"Azat","family":"Azamat","sequence":"first","affiliation":[{"name":"UNIST, Ulsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaewoo","family":"Park","sequence":"additional","affiliation":[{"name":"UNIST, Ulsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongeun","family":"Lee","sequence":"additional","affiliation":[{"name":"UNIST, Ulsan, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Quarry: Quantization-based ADC Reduction for ReRAM-based Deep Neural Network Accelerators. In 2021 IEEE\/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 1--7.","author":"Azamat Azat","year":"2021","unstructured":"Azat Azamat, Faaiz Asim, and Jongeun Lee. 2021. Quarry: Quantization-based ADC Reduction for ReRAM-based Deep Neural Network Accelerators. In 2021 IEEE\/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 1--7."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00017"},{"key":"e_1_3_2_1_3_1","volume-title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. arXiv preprint arXiv:1602.02830","author":"Courbariaux Matthieu","year":"2016","unstructured":"Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. arXiv preprint arXiv:1602.02830 (2016)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2019.102872"},{"key":"e_1_3_2_1_5_1","volume-title":"Learned Step Size Quantization. arXiv preprint arXiv:1902.08153","author":"Esser Steven K","year":"2019","unstructured":"Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha. 2019. Learned Step Size Quantization. arXiv preprint arXiv:1902.08153 (2019)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3058110"},{"key":"e_1_3_2_1_8_1","volume-title":"Successive Log Quantization for Cost-Efficient Neural Networks Using Stochastic Computing. In 2019 56th ACM\/IEEE Design Automation Conference (DAC). IEEE, 1--6.","author":"Lee Sugil","year":"2019","unstructured":"Sugil Lee, Hyeonuk Sim, Jooyeon Choi, and Jongeun Lee. 2019. Successive Log Quantization for Cost-Efficient Neural Networks Using Stochastic Computing. In 2019 56th ACM\/IEEE Design Automation Conference (DAC). IEEE, 1--6."},{"key":"e_1_3_2_1_9_1","volume-title":"Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks. arXiv preprint arXiv:1909.13144","author":"Li Yuhang","year":"2019","unstructured":"Yuhang Li, Xin Dong, and Wei Wang. 2019. Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks. arXiv preprint arXiv:1909.13144 (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2019.2928962"},{"key":"e_1_3_2_1_12_1","volume-title":"WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. In International Conference on Learning Representations ICLR","author":"Ni Renkun","year":"2021","unstructured":"Renkun Ni, Hong-min Chu, Oscar Casta\u00f1eda Fern\u00e1ndez, Ping-yeh Chiang, Christoph Studer, and Tom Goldstein. 2021. WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic. In International Conference on Learning Representations ICLR 2021. OpenReview."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPT.2016.7929192"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_39"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00080"},{"key":"e_1_3_2_1_16_1","volume-title":"Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. arXiv preprint arXiv:1901.06588","author":"Sakr Charbel","year":"2019","unstructured":"Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, and Kailash Gopalakrishnan. 2019. Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks. arXiv preprint arXiv:1901.06588 (2019)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021744"},{"key":"e_1_3_2_1_18_1","volume-title":"Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware. In International Conference on Learning Representations ICLR","author":"Zhao Xiandong","year":"2020","unstructured":"Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, and Lei Zhang. 2020. Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware. In International Conference on Learning Representations ICLR 2020. OpenReview."}],"event":{"name":"ICCAD '22: IEEE\/ACM International Conference on Computer-Aided Design","location":"San Diego California","acronym":"ICCAD '22","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-EDS Electronic Devices Society","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the 41st IEEE\/ACM International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549418","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508352.3549418","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:57Z","timestamp":1750186977000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,30]]},"references-count":18,"alternative-id":["10.1145\/3508352.3549418","10.1145\/3508352"],"URL":"https:\/\/doi.org\/10.1145\/3508352.3549418","relation":{},"subject":[],"published":{"date-parts":[[2022,10,30]]},"assertion":[{"value":"2022-12-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}