{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:51:41Z","timestamp":1774367501646,"version":"3.50.1"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1109\/iscas51556.2021.9401155","type":"proceedings-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:33:36Z","timestamp":1619559216000},"page":"1-5","source":"Crossref","is-referenced-by-count":5,"title":["ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator"],"prefix":"10.1109","author":[{"given":"Zhuoran","family":"Song","sequence":"first","affiliation":[]},{"given":"Dongyue","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhezhi","family":"He","sequence":"additional","affiliation":[]},{"given":"Xiaoyao","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"A unified framework of dnn weight pruning and weight clustering\/quantization using admm","author":"ye","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304076"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref15","article-title":"Shufflenet v2: Practical guidelines for efficient cnn architecture design","author":"ma","year":"2018","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref17","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref18","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref19","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Neural Information Processing Systems (NeurIPS)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2874823"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342009"},{"key":"ref6","article-title":"Pimprune: fine-grain dcnn pruning for crossbar-based process-in-memory architecture","author":"chu","year":"2020","journal-title":"Design Automation Conference (DAC)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3287715"},{"key":"ref8","article-title":"Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions","author":"wu","year":"2018","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3307650.3322271"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3196116"},{"key":"ref1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2015","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref9","article-title":"Soft weight-sharing for neural network compression","author":"ullrich","year":"2017","journal-title":"International Conference on Learning Representations (ICLR)"}],"event":{"name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","location":"Daegu, Korea","start":{"date-parts":[[2021,5,22]]},"end":{"date-parts":[[2021,5,28]]}},"container-title":["2021 IEEE International Symposium on Circuits and Systems (ISCAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9401028\/9401051\/09401155.pdf?arnumber=9401155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:44:02Z","timestamp":1652197442000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9401155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/iscas51556.2021.9401155","relation":{},"subject":[],"published":{"date-parts":[[2021,5]]}}}