{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:26:51Z","timestamp":1727065611392},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61834002"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB2202101"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Solid-State Circuits"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1109\/jssc.2021.3113569","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T01:51:23Z","timestamp":1634176283000},"page":"1542-1557","source":"Crossref","is-referenced-by-count":16,"title":["A 12.1 TOPS\/W Quantized Network Acceleration Processor With Effective-Weight-Based Convolution and Error-Compensation-Based Prediction"],"prefix":"10.1109","volume":"57","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3373-7178","authenticated-orcid":false,"given":"Huiyu","family":"Mo","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"given":"Wenping","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3618-5273","authenticated-orcid":false,"given":"Wenjing","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3615-6755","authenticated-orcid":false,"given":"Ang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2309-572X","authenticated-orcid":false,"given":"Shouyi","family":"Yin","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5117-7920","authenticated-orcid":false,"given":"Shaojun","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7548-4116","authenticated-orcid":false,"given":"Leibo","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIC.2019.8778193"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2018.8310261"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref32","first-page":"134","article-title":"A 3.4-to-13.3TOPS\/W 3.6TOPS dual-core deep-learning accelerator for versatile ai applications in 7 nm 5G smartphone SoC","author":"lin","year":"2020","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/VLSIC.2018.8502404"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2018.8310262"},{"key":"ref37","first-page":"5","article-title":"NVIDIA 8-bit inference width TensorRT","volume":"2","author":"migacz","year":"2017","journal-title":"Proc GPU Technol Conf"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3123939.3123982"},{"key":"ref35","year":"2016","journal-title":"DDR4 Spec—Micron Technology"},{"key":"ref34","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of the Symposium on Operating Systems Design and Implementation (OSDI)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2018.8310263"},{"key":"ref40","article-title":"Trained ternary quantization","author":"zhu","year":"2016","journal-title":"arXiv 1612 01064"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2017.7870354"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304028"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304041"},{"key":"ref14","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783725"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00062"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref28","first-page":"1","article-title":"Understanding convolutional neural networks with information theory: An initial exploration","volume":"32","author":"yu","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref4","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"2014","author":"simonyan","year":"2014","journal-title":"Comput Sci"},{"key":"ref27","article-title":"Exploiting cyclic symmetry in convolutional neural networks","author":"dieleman","year":"2016","journal-title":"arXiv 1602 02660"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2865834"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2955463"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2897937.2897995"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304076"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2017.2749425"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2018.2881913"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2865230"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2017.29"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2826536"},{"key":"ref46","year":"2019","journal-title":"TSMC 2019 Annual Report"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062244"},{"key":"ref45","first-page":"132","article-title":"7.2 A 20.5TOPS and 217.3GOPS\/mm² multicore SoC with DNN accelerator and image signal processor complying with ISO26262 for automotive applications","author":"yamada","year":"2019","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref22","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref21","first-page":"146","article-title":"9.2 A 28 nm 12.1TOPS\/W dual-mode CNN processor using effective-weight-based convolution and error-compensation-based prediction","author":"mo","year":"2021","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIC.2019.8778193"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00068"},{"key":"ref41","article-title":"Incremental network quantization: Towards lossless CNNs with low-precision weights","author":"zhou","year":"2017","journal-title":"arXiv 1702 03044"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00061"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2019.8662447"},{"key":"ref26","first-page":"2217","article-title":"Understanding and improving convolutional neural networks via concatenated rectified linear units","author":"shang","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","first-page":"130","article-title":"An 11.5TOPS\/W 1024-MAC butterfly structure dual-core sparsity-aware neural processing unit in 8 nm flagship mobile SoC","author":"song","year":"2019","journal-title":"IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers"},{"key":"ref25","first-page":"2990","article-title":"Group equivariant convolutional networks","author":"cohen","year":"2016","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Journal of Solid-State Circuits"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4\/9761850\/09570111.pdf?arnumber=9570111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T21:59:07Z","timestamp":1653947947000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9570111\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5]]},"references-count":46,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/jssc.2021.3113569","relation":{},"ISSN":["0018-9200","1558-173X"],"issn-type":[{"value":"0018-9200","type":"print"},{"value":"1558-173X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5]]}}}