{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T18:03:30Z","timestamp":1792951410868,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,6,24]],"date-time":"2018-06-24T00:00:00Z","timestamp":1529798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,6,24]]},"DOI":"10.1145\/3195970.3196129","type":"proceedings-article","created":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T09:54:59Z","timestamp":1529402099000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["Thundervolt"],"prefix":"10.1145","author":[{"given":"Jeff","family":"Zhang","sequence":"first","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kartheek","family":"Rangineni","sequence":"additional","affiliation":[{"name":"IIT Kanpur"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Ghodsi","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddharth","family":"Garg","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,6,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.11"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Manoj Alwani et al. 2016. Fused-layer CNN accelerators. In MICRO. 1--12.   Manoj Alwani et al. 2016. Fused-layer CNN accelerators. In MICRO . 1--12.","DOI":"10.1109\/MICRO.2016.7783725"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Arash Ardakani et al. 2017. VLSI implementation of deep neural network using integral stochastic computing. IEEE VLSI (2017).  Arash Ardakani et al. 2017. VLSI implementation of deep neural network using integral stochastic computing. IEEE VLSI (2017).","DOI":"10.1109\/TVLSI.2017.2654298"},{"key":"e_1_3_2_1_4_1","unstructured":"Jimmy Ba et al. 2014. Do deep nets really need to be deep?. In NIPS. 2654--2662.   Jimmy Ba et al. 2014. Do deep nets really need to be deep?. In NIPS . 2654--2662."},{"key":"e_1_3_2_1_5_1","unstructured":"Ana Margarida Cachopo etal 2007. Improving methods for single-label text categorization. Instituto Superior T\u00e9cnico Portugal (2007).  Ana Margarida Cachopo et al. 2007. Improving methods for single-label text categorization. Instituto Superior T\u00e9cnico Portugal (2007)."},{"key":"e_1_3_2_1_6_1","volume-title":"Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks","author":"Chen Yu-Hsin","year":"2017"},{"key":"e_1_3_2_1_7_1","volume-title":"TIMBER: Time borrowing and error relaying for online timing error resilience","author":"Mihir Choudhury","year":"2010"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Shidhartha Das et al. 2009. RazorII: In situ error detection and correction for PVT and SER tolerance. IEEE JSCC (2009) 32--48.  Shidhartha Das et al. 2009. RazorII: In situ error detection and correction for PVT and SER tolerance. IEEE JSCC (2009) 32--48.","DOI":"10.1109\/JSSC.2008.2007145"},{"key":"e_1_3_2_1_9_1","volume-title":"Imagenet: A large-scale hierarchical image database","author":"Jia Deng","year":"2009"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2004.85"},{"key":"e_1_3_2_1_11_1","volume-title":"Bubble Razor: An architecture-independent approach to timing-error detection and correction","author":"Matthew Fojtik","year":"2012"},{"key":"e_1_3_2_1_12_1","volume-title":"Blueshift: Designing processors for timing speculation from the ground up.","author":"Brian Greskamp","year":"2009"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of ICML. 1737--1746","author":"Suyog"},{"key":"e_1_3_2_1_14_1","unstructured":"Song Han et al. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).  Song Han et al. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_1_15_1","unstructured":"Geoffrey Hinton et al. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012).  Geoffrey Hinton et al. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of ICCAD.","author":"Xun"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_18_1","unstructured":"Alex Krizhevsky et al. 2014. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997 (2014).  Alex Krizhevsky et al. 2014. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997 (2014)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.1982.1653825"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of DATE.","author":"Seogoo"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2024724.2024760"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vlsi.2017.10.013"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of DATE. 1393--1398","author":"Atul"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.32"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037746"},{"key":"e_1_3_2_1_27_1","unstructured":"Jonathan Ross et al. 2016. Neural Network Processor. (2016).  Jonathan Ross et al. 2016. Neural Network Processor. (2016)."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of DATE. 145--150","author":"Shakib Syed"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of ICML. 1058--1066","author":"Li"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062207"},{"key":"e_1_3_2_1_32_1","unstructured":"Wei Wen et al. 2016. Learning structured sparsity in deep neural networks. In NIPS. 2074--2082.   Wei Wen et al. 2016. Learning structured sparsity in deep neural networks. In NIPS. 2074--2082."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2012.2202930"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Paul N Whatmough etal 2017. 14.3 A 28nm SoC with a 1.2 GHz 568nJ\/prediction sparse deep-neural-network engine with &gt; 0.1 timing error rate tolerance for IoT applications. In Processings of IEEE ISSCC. 242--243.  Paul N Whatmough et al. 2017. 14.3 A 28nm SoC with a 1.2 GHz 568nJ\/prediction sparse deep-neural-network engine with &gt; 0.1 timing error rate tolerance for IoT applications. In Processings of IEEE ISSCC. 242--243.","DOI":"10.1109\/ISSCC.2017.7870351"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897937.2898102"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080215"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of DATE. 922--925","author":"Jeff"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of VTS.","author":"Jeff"}],"event":{"name":"DAC '18: The 55th Annual Design Automation Conference 2018","location":"San Francisco California","acronym":"DAC '18","sponsor":["EDAC Electronic Design Automation Consortium","SIGDA ACM Special Interest Group on Design Automation","IEEE Council on Electronic Design Automation (CEDA)","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 55th Annual Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3195970.3196129","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3195970.3196129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:07:40Z","timestamp":1750198060000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3195970.3196129"}},"subtitle":["enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelerators"],"short-title":[],"issued":{"date-parts":[[2018,6,24]]},"references-count":38,"alternative-id":["10.1145\/3195970.3196129","10.1145\/3195970"],"URL":"https:\/\/doi.org\/10.1145\/3195970.3196129","relation":{},"subject":[],"published":{"date-parts":[[2018,6,24]]},"assertion":[{"value":"2018-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}