{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:50:23Z","timestamp":1771703423871,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T00:00:00Z","timestamp":1657411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61832020, Grant No. 62032001, Grant No. 92064006"],"award-info":[{"award-number":["Grant No. 61832020, Grant No. 62032001, Grant No. 92064006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Academy of Artificial Intelligence"},{"name":"National Key R&D Program of China","award":["2020AAA0105200"],"award-info":[{"award-number":["2020AAA0105200"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,10]]},"DOI":"10.1145\/3489517.3530500","type":"proceedings-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T23:19:29Z","timestamp":1661296769000},"page":"1009-1014","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Tailor"],"prefix":"10.1145","author":[{"given":"Xingchen","family":"Li","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Zhihang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Guangyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Reliance Memory Ltd., Hefei, China"}]},{"given":"Zhichao","family":"Lu","sequence":"additional","affiliation":[{"name":"Reliance Memory Ltd., Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Maarten Baert and Wim Dehaene. 2019. 20.1 A 5GS\/s 7.2 ENOB Time-Interleaved VCO-Based ADC Achieving 30.5 fJ\/conv-step. In ISSCC.","DOI":"10.1109\/ISSCC.2019.8662412"},{"key":"e_1_3_2_1_2_1","volume-title":"Deep Learning With Edge Computing: A Review. Proc","author":"Chen Jiasi","year":"2019","unstructured":"Jiasi Chen and Xukan Ran. 2019. Deep Learning With Edge Computing: A Review. Proc. IEEE (2019)."},{"key":"e_1_3_2_1_3_1","volume-title":"Cascade: Connecting rrams to extend analog dataflow in an end-to-end in-memory processing paradigm. In MICRO.","author":"Chou Teyuh","year":"2019","unstructured":"Teyuh Chou, Wei Tang, et al. 2019. Cascade: Connecting rrams to extend analog dataflow in an end-to-end in-memory processing paradigm. In MICRO."},{"key":"e_1_3_2_1_4_1","volume-title":"Challenges for future computing systems. HiPEAC keynote","author":"Dally WJ","year":"2015","unstructured":"WJ Dally. 2015. Challenges for future computing systems. HiPEAC keynote (2015)."},{"key":"e_1_3_2_1_5_1","unstructured":"Rundong Li Yan Wang et al. 2019. Fully quantized network for object detection. In CVPR."},{"key":"e_1_3_2_1_6_1","volume-title":"Timely: Pushing data movements and interfaces in pim accelerators towards local and in time domain. In ISCA.","author":"Li Weitao","year":"2020","unstructured":"Weitao Li, Pengfei Xu, et al. 2020. Timely: Pushing data movements and interfaces in pim accelerators towards local and in time domain. In ISCA."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Qi Liu Bin Gao et al. 2020. 33.2 A fully integrated analog ReRAM based 78.4 TOPS\/W compute-in-memory chip with fully parallel MAC computing. In ISSCC.","DOI":"10.1109\/ISSCC19947.2020.9062953"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Naveen Muralimanohar Rajeev Balasubramonian and Norm Jouppi. 2007. Optimizing NUCA organizations and wiring alternatives for large caches with CACTI 6.0. In MICRO.","DOI":"10.1109\/MICRO.2007.33"},{"key":"e_1_3_2_1_9_1","unstructured":"B Murmann. 2015. ADC Performance Survey 1997--2021 (ISSCC & VLSI Symposium). https:\/\/web.stanford.edu\/~murmann\/adcsurvey.html."},{"key":"e_1_3_2_1_10_1","unstructured":"NCSU. [n. d.]. FreePDK45. https:\/\/www.eda.ncsu.edu\/wiki\/FreePDK45:Contents."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Ximing Qiao Xiong Cao et al. 2018. Atomlayer: a universal reram-based cnn accelerator with atomic layer computation. In DAC.","DOI":"10.1145\/3195970.3195998"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Mehdi Saberi Reza Lotfi et al. 2011. Analysis of power consumption and linearity in capacitive digital-to-analog converters used in successive approximation ADCs. IEEE Transactions on Circuits and Systems I: Regular Papers (2011).","DOI":"10.1109\/TCSI.2011.2107214"},{"key":"e_1_3_2_1_13_1","volume-title":"ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars. ACM SIGARCH Computer Architecture News","author":"Shafiee Ali","year":"2016","unstructured":"Ali Shafiee, Anirban Nag, et al. 2016. ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars. ACM SIGARCH Computer Architecture News (2016)."},{"key":"e_1_3_2_1_14_1","volume-title":"Pipelayer: A pipelined reram-based accelerator for deep learning. In HPCA.","author":"Song Linghao","year":"2017","unstructured":"Linghao Song, Xuehai Qian, et al. 2017. Pipelayer: A pipelined reram-based accelerator for deep learning. In HPCA."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Hanbo Sun Zhenhua Zhu et al. 2020. An energy-efficient quantized and regularized training framework for processing-in-memory accelerators. In ASP-DAC.","DOI":"10.1109\/ASP-DAC47756.2020.9045192"},{"key":"e_1_3_2_1_16_1","volume-title":"AEPE: An area and power efficient RRAM crossbar-based accelerator for deep CNNs. In NVMSA.","author":"Tang Shibin","year":"2017","unstructured":"Shibin Tang, Shouyi Yin, et al. 2017. AEPE: An area and power efficient RRAM crossbar-based accelerator for deep CNNs. In NVMSA."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Peng Yao Huaqiang Wu et al. 2020. Fully hardware-implemented memristor convolutional neural network. Nature (2020).","DOI":"10.1038\/s41586-020-1942-4"}],"event":{"name":"DAC '22: 59th ACM\/IEEE Design Automation Conference","location":"San Francisco California","acronym":"DAC '22","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the 59th ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3489517.3530500","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3489517.3530500","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:17Z","timestamp":1750186937000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3489517.3530500"}},"subtitle":["removing redundant operations in memristive analog neural network accelerators"],"short-title":[],"issued":{"date-parts":[[2022,7,10]]},"references-count":17,"alternative-id":["10.1145\/3489517.3530500","10.1145\/3489517"],"URL":"https:\/\/doi.org\/10.1145\/3489517.3530500","relation":{},"subject":[],"published":{"date-parts":[[2022,7,10]]},"assertion":[{"value":"2022-08-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}