{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:27:38Z","timestamp":1779380858063,"version":"3.53.1"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62274008"],"award-info":[{"award-number":["62274008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L223004"],"award-info":[{"award-number":["L223004"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/tcad.2024.3358609","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T18:29:55Z","timestamp":1706207395000},"page":"2084-2097","source":"Crossref","is-referenced-by-count":10,"title":["CIM\u00b2PQ: An Arraywise and Hardware-Friendly Mixed Precision Quantization Method for Analog Computing-In-Memory"],"prefix":"10.1109","volume":"43","author":[{"given":"Sifan","family":"Sun","sequence":"first","affiliation":[{"name":"National Key Lab of Spintronics, International Innovation Institute, Beihang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9369-0327","authenticated-orcid":false,"given":"Jinyu","family":"Bai","sequence":"additional","affiliation":[{"name":"National Key Lab of Spintronics, International Innovation Institute, Beihang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyu","family":"Shi","sequence":"additional","affiliation":[{"name":"National Key Lab of Spintronics, International Innovation Institute, Beihang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8088-0404","authenticated-orcid":false,"given":"Weisheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Key Lab of Spintronics, International Innovation Institute, Beihang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3169-6034","authenticated-orcid":false,"given":"Wang","family":"Kang","sequence":"additional","affiliation":[{"name":"National Key Lab of Spintronics, International Innovation Institute, Beihang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref5","first-page":"802","article-title":"Floatpim: In-memory acceleration of deep neural network training with high precision","volume-title":"Proc. IEEE Int. Symp. Comput. Architect.","author":"Imani"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s11671-020-03299-9"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2017.107"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3386263.3407649"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2021.3092533"},{"key":"ref10","article-title":"DoReFa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"Zhou","year":"2016","journal-title":"arXiv:1606.06160"},{"key":"ref11","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018","journal-title":"arXiv:1805.06085"},{"key":"ref12","article-title":"BRECQ: Pushing the limit of post-training quantization by block reconstruction","author":"Li","year":"2021","journal-title":"arXiv:2102.05426"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.045"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3583781.3590292"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_16"},{"key":"ref16","first-page":"372","article-title":"Mixed precision quantization for ReRAM-based DNN inference accelerators","volume-title":"Proc. Asia South Pac. Design Autom. Conf.","author":"Huang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218737"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2021.3127129"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2281535"},{"key":"ref20","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref24","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":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.761"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.574"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref29","article-title":"Ternary weight networks","author":"Li","year":"2016","journal-title":"arXiv:1605.04711"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref31","first-page":"2849","article-title":"Fixed point quantization of deep convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lin"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00038"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3490422.3502364"},{"key":"ref34","article-title":"Mixed precision quantization of convnets via differentiable neural architecture search","author":"Wu","year":"2018","journal-title":"arXiv:1812.00090"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3166792"},{"key":"ref37","article-title":"Constrained K-means clustering","author":"Bradley","year":"2000"},{"key":"ref38","article-title":"VEGA: Towards an end-to-end configurable AutoML pipeline","author":"Wang","year":"2020","journal-title":"arXiv:2011.01507"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247982"},{"key":"ref40","article-title":"Improving post training neural quantization: Layer-wise calibration and integer programming","author":"Hubara","year":"2020","journal-title":"arXiv:2006.10518"},{"key":"ref41","first-page":"7950","article-title":"Post training 4-bit quantization of convolutional networks for rapid-deployment","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Banner"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413631"},{"key":"ref43","first-page":"43","article-title":"A quantized training framework for robust and accurate ReRAM-based neural network accelerators","volume-title":"Proc. Asia South Pac. Design Autom. Conf.","author":"Zhang"},{"key":"ref44","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref45","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM19573.2019.8993491"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2307\/2332226"}],"container-title":["IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/43\/10564796\/10414004.pdf?arnumber=10414004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T20:07:36Z","timestamp":1719346056000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10414004\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":48,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tcad.2024.3358609","relation":{},"ISSN":["0278-0070","1937-4151"],"issn-type":[{"value":"0278-0070","type":"print"},{"value":"1937-4151","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}