{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:22:31Z","timestamp":1740169351712,"version":"3.37.3"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"KAKENHI, Japan","doi-asserted-by":"publisher","award":["23H05489","22KJ1348"],"award-info":[{"award-number":["23H05489","22KJ1348"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2023.3347578","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T19:30:14Z","timestamp":1703619014000},"page":"2057-2073","source":"Crossref","is-referenced-by-count":1,"title":["Pianissimo: A Sub-mW Class DNN Accelerator With Progressively Adjustable Bit-Precision"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5273-8448","authenticated-orcid":false,"given":"Junnosuke","family":"Suzuki","sequence":"first","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6639-7694","authenticated-orcid":false,"given":"Jaehoon","family":"Yu","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mari","family":"Yasunaga","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3206-1479","authenticated-orcid":false,"given":"\u00c1ngel L\u00f3pez","family":"Garc\u00eda-Arias","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8472-7841","authenticated-orcid":false,"given":"Yasuyuki","family":"Okoshi","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5608-5666","authenticated-orcid":false,"given":"Shungo","family":"Kumazawa","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8648-3768","authenticated-orcid":false,"given":"Kota","family":"Ando","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0795-2974","authenticated-orcid":false,"given":"Kazushi","family":"Kawamura","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiem","family":"Van Chu","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-1252","authenticated-orcid":false,"given":"Masato","family":"Motomura","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2019.2918951"},{"key":"ref2","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018","journal-title":"arXiv:1805.06085"},{"key":"ref3","article-title":"Learned step size quantization","author":"Esser","year":"2019","journal-title":"arXiv:1902.08153"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00242"},{"key":"ref6","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"Han","year":"2015","journal-title":"arXiv:1510.00149"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00029"},{"key":"ref8","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref10","first-page":"1","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cai"},{"key":"ref11","first-page":"517","article-title":"MicroNets: Neural network architectures for deploying TinyML applications on commodity microcontrollers","volume-title":"Proc. Int. Conf. Mach. Learn. Syst.","author":"Banbury"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783722"},{"key":"ref14","first-page":"382","article-title":"Bit-pragmatic deep neural network computing","volume-title":"Proc. 50th Annu. IEEE\/ACM Int. Symp. Microarchitecture (MICRO)","author":"Albericio"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00069"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2018.2865489"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304041"},{"key":"ref18","first-page":"304","article-title":"Laconic deep learning inference acceleration","volume-title":"Proc. ACM\/IEEE 46th Annu. Int. Symp. Comput. Archit. (ISCA)","author":"Sharify"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480123"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO56248.2022.00097"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/isscc42615.2023.10067643"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42615.2023.10067615"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC19947.2020.9063000"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42613.2021.9365963"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.23919\/VLSICircuits52068.2021.9492401"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/VLSITechnologyandCir46769.2022.9830409"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42615.2023.10067646"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.23919\/VLSITechnologyandCir57934.2023.10185297"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/wacv56688.2023.00381"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2731301"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.23919\/VLSITechnologyandCir57934.2023.10185293"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.15803\/ijnc.11.2_338"},{"key":"ref33","first-page":"1","article-title":"MLPerf tiny benchmark","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Banbury"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.23919\/VLSICircuits52068.2021.9492338"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358291"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS51385.2021.00043"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00222"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00514"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2018.8310196"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42614.2022.9731668"},{"key":"ref42","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \u22121","author":"Courbariaux","year":"2016","journal-title":"arXiv:1602.02830"},{"key":"ref43","first-page":"11711","article-title":"MCUNet: Tiny deep learning on IoT devices","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lin"},{"key":"ref44","first-page":"2346","article-title":"MCUNetV2: Memory-efficient patch-based inference for tiny deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Lin"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.33682\/m76f-d618"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref47","article-title":"Visual wake words dataset","author":"Chowdhery","year":"2019","journal-title":"arXiv:1906.05721"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC19947.2020.9063149"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2019.2937437"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10374335.pdf?arnumber=10374335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T04:09:35Z","timestamp":1706069375000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10374335\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3347578","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}