{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:15:09Z","timestamp":1768342509248,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":14,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018227","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018227","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,20]]},"DOI":"10.1145\/3658617.3697697","type":"proceedings-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:32:21Z","timestamp":1741098741000},"page":"1174-1180","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["RISC-V Driven Orchestration of Vector Processing Units and eFlash Compute-in-Memory Arrays for Fast and Accurate Keyword Spotting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1910-4489","authenticated-orcid":false,"given":"Gunil","family":"Kang","sequence":"first","affiliation":[{"name":"Korea University & Samsung Electronics, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8047-2416","authenticated-orcid":false,"given":"Dahoon","family":"Park","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1194-1492","authenticated-orcid":false,"given":"Hojin","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1150-9888","authenticated-orcid":false,"given":"Sangwoo","family":"Jung","sequence":"additional","affiliation":[{"name":"Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3435-7468","authenticated-orcid":false,"given":"Jiyong","family":"Park","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2041-617X","authenticated-orcid":false,"given":"Jung Gyu","family":"Min","sequence":"additional","affiliation":[{"name":"Pohang University of Science and Technology, Pohang, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-8276","authenticated-orcid":false,"given":"Youngjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Pohang University of Science and Technology, Pohang, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-8602","authenticated-orcid":false,"given":"Jaeha","family":"Kung","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CICC57935.2023.10121209"},{"key":"e_1_3_2_1_2_1","volume-title":"Proc. IEEE\/CVF CVPR. 13733--13742","author":"Xiaohan","unstructured":"Xiaohan Ding et al. 2021. RepVGG: Making VGG-style convnets great again. In Proc. IEEE\/CVF CVPR. 13733--13742."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIT.2019.8776557"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA52012.2021.00010"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIC.2019.8778160"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2021.3098671"},{"key":"e_1_3_2_1_7_1","first-page":"22941","article-title":"On-device training under 256kb memory","volume":"35","author":"Ji Lin","year":"2022","unstructured":"Ji Lin et al. 2022. On-device training under 256kb memory. Proc. NeurIPS 35 (2022), 22941--22954.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. ECCV. 722--737","author":"Zechun","unstructured":"Zechun Liu et al. 2018. Bi-Real Net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In Proc. ECCV. 722--737."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2021-1966"},{"key":"e_1_3_2_1_10_1","volume-title":"Proc. IJCAI. 4346--4352","author":"Haotong","unstructured":"Haotong Qin et al. 2022. BiFSMN: Binary Neural Network for Keyword Spotting. In Proc. IJCAI. 4346--4352."},{"key":"e_1_3_2_1_11_1","volume-title":"BiFSMNv2: Pushing binary neural networks for keyword spotting to real-network performance","author":"Qin Haotong","year":"2023","unstructured":"Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Zejun Ma, Jiakai Wang, Jie Luo, and Xianglong Liu. 2023. BiFSMNv2: Pushing binary neural networks for keyword spotting to real-network performance. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"key":"e_1_3_2_1_12_1","volume-title":"Speech commands: A dataset for limited-vocabulary speech recognition. arXiv:1804.03209","author":"Warden Pete","year":"2018","unstructured":"Pete Warden. 2018. Speech commands: A dataset for limited-vocabulary speech recognition. arXiv:1804.03209 (2018)."},{"key":"e_1_3_2_1_13_1","volume-title":"TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices. arXiv:2008.04245","author":"Wong Alexander","year":"2020","unstructured":"Alexander Wong, Mahmoud Famouri, Maya Pavlova, and Siddharth Surana. 2020. TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices. arXiv:2008.04245 (2020)."},{"key":"e_1_3_2_1_14_1","volume-title":"Proc. IEEE ICASSP. 5869--5873","author":"Shiliang","unstructured":"Shiliang Zhang et al. 2018. Deep-FSMN for large vocabulary continuous speech recognition. In Proc. IEEE ICASSP. 5869--5873."}],"event":{"name":"ASPDAC '25: 30th Asia and South Pacific Design Automation Conference","location":"Tokyo Japan","acronym":"ASPDAC '25","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEICE","IPSJ","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the 30th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697697","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3658617.3697697","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:49Z","timestamp":1750295869000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":14,"alternative-id":["10.1145\/3658617.3697697","10.1145\/3658617"],"URL":"https:\/\/doi.org\/10.1145\/3658617.3697697","relation":{},"subject":[],"published":{"date-parts":[[2025,1,20]]},"assertion":[{"value":"2025-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}