{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:55:02Z","timestamp":1781794502725,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2202310"],"award-info":[{"award-number":["2202310"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3787109.3815282","type":"proceedings-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:17:19Z","timestamp":1781792239000},"page":"164-170","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Lottery Ticket Hypothesis: Toward Efficient and Robust Deep Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2575-0142","authenticated-orcid":false,"given":"Ruixuan","family":"Wang","sequence":"first","affiliation":[{"name":"ECE, Villanova University, Villanova University, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7851-4032","authenticated-orcid":false,"given":"Jinghao","family":"Wen","sequence":"additional","affiliation":[{"name":"ECE, Villanova University, Villanova, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4476-2501","authenticated-orcid":false,"given":"Xun","family":"Jiao","sequence":"additional","affiliation":[{"name":"ECE, Villanova University, Villanova, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2019.00034"},{"key":"e_1_3_3_1_3_2","unstructured":"Tianlong Chen Jonathan Frankle Shiyu Chang Sijia Liu Yang Zhang Zhangyang Wang and Michael Carbin. 2020. The lottery ticket hypothesis for pre-trained bert networks. Advances in neural information processing systems 33 (2020) 15834\u201315846."},{"key":"e_1_3_3_1_4_2","first-page":"1695","volume-title":"International conference on machine learning","author":"Chen Tianlong","year":"2021","unstructured":"Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021. A unified lottery ticket hypothesis for graph neural networks. In International conference on machine learning. PMLR, 1695\u20131706."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_3_1_6_2","unstructured":"Jonathan Frankle et\u00a0al. 2018. The lottery ticket hypothesis: Finding sparse trainable neural networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.03635 (2018)."},{"key":"e_1_3_3_1_7_2","volume-title":"End-to-end CNN optimization for low-complexity acoustic scene classification in the DCASE 2021 challenge","author":"Galindo-Meza Carlos\u00a0A","year":"2021","unstructured":"Carlos\u00a0A Galindo-Meza, Juan\u00a0A del Hoyo\u00a0Ontiveros, JI\u00a0Torres Ortega, and Paulo Lopez-Meyer. 2021. End-to-end CNN optimization for low-complexity acoustic scene classification in the DCASE 2021 challenge. Technical Report. DCASE2021 Challenge, Tech. Rep."},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Amir Gholami Zhewei Yao Sehoon Kim Coleman Hooper Michael\u00a0W Mahoney and Kurt Keutzer. 2024. AI and memory wall. IEEE Micro (2024).","DOI":"10.1109\/MM.2024.3373763"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00033"},{"key":"e_1_3_3_1_12_2","unstructured":"Dhiraj Kalamkar et\u00a0al. 2019. A study of BFLOAT16 for deep learning training. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1905.12322 (2019)."},{"key":"e_1_3_3_1_13_2","unstructured":"Alex Krizhevsky Geoffrey Hinton et\u00a0al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Yann LeCun et\u00a0al. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86 11 (1998) 2278\u20132324.","DOI":"10.1109\/5.726791"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Robert\u00a0E Lyons et\u00a0al. 1962. The use of triple-modular redundancy to improve computer reliability. IBM journal of research and development 6 2 (1962) 200\u2013209.","DOI":"10.1147\/rd.62.0200"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Dongning Ma et\u00a0al. 2021. Devot: Dynamic delay modeling of functional units under voltage and temperature variations. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 41 4 (2021) 827\u2013839.","DOI":"10.1109\/TCAD.2021.3076970"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE52982.2021.00025"},{"key":"e_1_3_3_1_18_2","first-page":"6682","volume-title":"International Conference on Machine Learning","author":"Malach Eran","year":"2020","unstructured":"Eran Malach et\u00a0al. 2020. Proving the lottery ticket hypothesis: Pruning is all you need. In International Conference on Machine Learning. PMLR, 6682\u20136691."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/MASCOTS.2017.12"},{"key":"e_1_3_3_1_20_2","unstructured":"Mansheej Paul Feng Chen Brett\u00a0W Larsen Jonathan Frankle Surya Ganguli and Gintare\u00a0Karolina Dziugaite. 2022. Unmasking the Lottery Ticket Hypothesis: What\u2019s Encoded in a Winning Ticket\u2019s Mask? arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2210.03044 (2022)."},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Aleksandar Radonjic. 2020. Integer Codes Correcting Single Errors and Detecting Burst Errors Within a Byte. IEEE Transactions on Device and Materials Reliability 20 4 (2020) 748\u2013753.","DOI":"10.1109\/TDMR.2020.3033511"},{"key":"e_1_3_3_1_22_2","first-page":"11893","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Ramanujan Vivek","year":"2020","unstructured":"Vivek Ramanujan et\u00a0al. 2020. What\u2019s hidden in a randomly weighted neural network?. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 11893\u201311902."},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3195997"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Michael Rogenmoser Yvan Tortorella Davide Rossi Francesco Conti and Luca Benini. 2025. Hybrid modular redundancy: Exploring modular redundancy approaches in RISC-V multi-core computing clusters for reliable processing in space. ACM Transactions on Cyber-Physical Systems 9 1 (2025) 1\u201329.","DOI":"10.1145\/3635161"},{"key":"e_1_3_3_1_25_2","unstructured":"Bita\u00a0Darvish Rouhani et\u00a0al. 2023. Microscaling data formats for deep learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.10537 (2023)."},{"key":"e_1_3_3_1_26_2","unstructured":"Karen Simonyan. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.1556 (2014)."},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.5555\/2388996.2389100"},{"key":"e_1_3_3_1_28_2","unstructured":"David Stutz et\u00a0al. 2021. Bit error robustness for energy-efficient dnn accelerators. Proceedings of Machine Learning and Systems 3 (2021) 569\u2013598."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480111"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2015.7056044"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD56317.2022.00086"}],"event":{"name":"GLSVLSI '26: Great Lakes Symposium on VLSI 2026","location":"Canandaigua , NY , USA","acronym":"GLSVLSI '26","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the Great Lakes Symposium on VLSI 2026"],"original-title":[],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:26:45Z","timestamp":1781792805000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3787109.3815282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":30,"alternative-id":["10.1145\/3787109.3815282","10.1145\/3787109"],"URL":"https:\/\/doi.org\/10.1145\/3787109.3815282","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}