{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:36:46Z","timestamp":1772303806019,"version":"3.50.1"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1900675"],"award-info":[{"award-number":["CCF-1900675"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation (SRC) and Defense Advanced Research Projects Agency (DARPA) through the Applications Driving Architectures (ADA) Research Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Emerg. Sel. Topics Circuits Syst."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1109\/jetcas.2022.3227471","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T20:39:43Z","timestamp":1670445583000},"page":"858-866","source":"Crossref","is-referenced-by-count":18,"title":["Exploring Compute-in-Memory Architecture Granularity for Structured Pruning of Neural Networks"],"prefix":"10.1109","volume":"12","author":[{"given":"Fan-Hsuan","family":"Meng","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-5936","authenticated-orcid":false,"given":"Xinxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9534-3134","authenticated-orcid":false,"given":"Ziyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric Yeu-Jer","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4731-1976","authenticated-orcid":false,"given":"Wei D.","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"arXiv 1605 07146"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586105"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465793"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3307650.3322271"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342009"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC47752.2019.9042000"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2021.3069011"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218523"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2874823"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3287715"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00367"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00339"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref20","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"krishnamoorthi","year":"2018","journal-title":"arXiv 1806 08342"},{"key":"ref22","article-title":"Exploring the regularity of sparse structure in convolutional neural networks","author":"mao","year":"2017","journal-title":"arXiv 1705 08922"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"ref24","first-page":"1","article-title":"Learning structured sparsity in deep neural networks","volume":"29","author":"wen","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.280"},{"key":"ref26","article-title":"Hierarchical block sparse neural networks","author":"vooturi","year":"2018","journal-title":"arXiv 1808 03420"},{"key":"ref25","article-title":"CondenseNet: An efficient DenseNet using learned group convolutions","author":"huang","year":"2017","journal-title":"arXiv 1711 09224"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202100199"},{"key":"ref51","first-page":"252","article-title":"16.4 An 89TOPS\/W and 16.3TOPS\/mm2 all-digital SRAM-based full-precision compute-in memory macro in 22 nm for machine-learning edge applications","volume":"64","author":"chih","year":"2021","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-021-00676-9"},{"key":"ref10","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2016","journal-title":"arXiv 1611 06440"},{"key":"ref11","article-title":"Sparsely-connected neural networks: Towards efficient VLSI implementation of deep neural networks","author":"ardakani","year":"2016","journal-title":"arXiv 1611 01427"},{"key":"ref40","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/URTC45901.2018.9244787"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2019.00051"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41586-020-1942-4","article-title":"Fully hardware-implemented memristor convolutional neural network","volume":"577","author":"yao","year":"2020","journal-title":"Nature"},{"key":"ref15","first-page":"559","article-title":"TAICHI: A tiled architecture for in-memory computing and heterogeneous integration","volume":"69","author":"wang","year":"2021","journal-title":"IEEE Trans Circuits Syst II Exp Briefs"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6463\/ab37b6"},{"key":"ref17","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2015","journal-title":"arXiv 1510 00149 [cs]"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref19","first-page":"6869","article-title":"Quantized neural networks: Training neural networks with low precision weights and activations","volume":"18","author":"hubara","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-017-0006-8"},{"key":"ref8","first-page":"1","article-title":"Optimal brain damage","volume":"2","author":"lecun","year":"1989","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/IRPS46558.2021.9405092"},{"key":"ref9","first-page":"1","article-title":"Learning both weights and connections for efficient neural network","volume":"28","author":"han","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/VLSITechnologyandCir46769.2022.9830490"},{"key":"ref45","author":"murmann","year":"2011","journal-title":"ADC Performance Survey 1997–2011"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-04159-x"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM19573.2019.8993641"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/JXCDC.2020.2992228"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001139"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783723"},{"key":"ref43","article-title":"Multiply accumulate operations in memristor crossbar arrays for analog computing","volume":"42","author":"chen","year":"2021","journal-title":"Semiconductors"}],"container-title":["IEEE Journal on Emerging and Selected Topics in Circuits and Systems"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/5503868\/9991262\/9973355-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5503868\/9991262\/09973355.pdf?arnumber=9973355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T19:10:26Z","timestamp":1673896226000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9973355\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":52,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/jetcas.2022.3227471","relation":{},"ISSN":["2156-3357","2156-3365"],"issn-type":[{"value":"2156-3357","type":"print"},{"value":"2156-3365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]}}}