{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:40:02Z","timestamp":1762177202250,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,22]],"date-time":"2019-06-22T00:00:00Z","timestamp":1561161600000},"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":[[2019,6,22]]},"DOI":"10.1145\/3307650.3322270","type":"proceedings-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T12:42:33Z","timestamp":1560516153000},"page":"567-578","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology"],"prefix":"10.1145","author":[{"given":"Ruizhe","family":"Cai","sequence":"first","affiliation":[{"name":"Northeastern University"}]},{"given":"Ao","family":"Ren","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Olivia","family":"Chen","sequence":"additional","affiliation":[{"name":"Yokohama National University, Japan"}]},{"given":"Ning","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Caiwen","family":"Ding","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Xuehai","family":"Qian","sequence":"additional","affiliation":[{"name":"University of Southern California"}]},{"given":"Jie","family":"Han","sequence":"additional","affiliation":[{"name":"University of Alberta, Canada"}]},{"given":"Wenhui","family":"Luo","sequence":"additional","affiliation":[{"name":"Yokohama National University, Japan"}]},{"given":"Nobuyuki","family":"Yoshikawa","sequence":"additional","affiliation":[{"name":"Yokohama National University, Japan"}]},{"given":"Yanzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University"}]}],"member":"320","published-online":{"date-parts":[[2019,6,22]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1145\/2465787.2465794"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1109\/TCAD.2017.2778107"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1109\/TVLSI.2017.2654298"},{"key":"e_1_3_2_1_4_1","volume-title":"Solid-State Circuits Conference (ISSCC)","author":"Bang Suyoung","year":"2017","unstructured":"Suyoung Bang, Jingcheng Wang, Ziyun Li, Cao Gao, Yejoong Kim, Qing Dong, Yen-Po Chen, Laura Fick, Xun Sun, Ron Dreslinski, Trevor Mudge, Hun Seok Kim, David Blaauw, and Dennis Sylvester. 2017. 14.7 A 288μW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence. In Solid-State Circuits Conference (ISSCC), 2017 IEEE International. IEEE, 250--251."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1109\/12.954505"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1145\/3173162.3173212"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1145\/2644865.2541967"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1109\/MICRO.2014.58"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/JSSC.2016.2616357"},{"volume-title":"The SQUID handbook: Applications of SQUIDs and SQUID systems","author":"Clarke John","unstructured":"John Clarke and Alex I Braginski. 2006. The SQUID handbook: Applications of SQUIDs and SQUID systems. John Wiley & Sons.","key":"e_1_3_2_1_10_1"},{"key":"e_1_3_2_1_11_1","volume-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830","author":"Courbariaux Matthieu","year":"2016","unstructured":"Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)."},{"key":"e_1_3_2_1_12_1","volume-title":"Solid-State Circuits Conference (ISSCC)","author":"Desoli Giuseppe","year":"2017","unstructured":"Giuseppe Desoli, Nitin Chawla, Thomas Boesch, Surinder-pal Singh, Elio Guidetti, Fabio De Ambroggi, Tommaso Majo, Paolo Zambotti, Manuj Ayodhyawasi, Harvinder Singh, and Nalin Aggarwal. 2017. 14.1 A 2.9 TOPS\/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems. In Solid-State Circuits Conference (ISSCC), 2017 IEEE International. IEEE, 238--239."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1145\/2749469.2750389"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1145\/1465482.1465505"},{"volume-title":"Advances in information systems science","author":"Gaines Brian R","unstructured":"Brian R Gaines. 1969. Stochastic computing systems. In Advances in information systems science. Springer, 37--172.","key":"e_1_3_2_1_15_1"},{"volume-title":"Random number generation and Monte Carlo methods","author":"Gentle James E","unstructured":"James E Gentle. 2006. Random number generation and Monte Carlo methods. Springer Science & Business Media.","key":"e_1_3_2_1_16_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1145\/3020078.3021745"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1145\/3007787.3001163"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.5555\/3195638.3195661"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1145\/3173162.3173176"},{"unstructured":"Yann LeCun and Corinna Cortes. 2010. MNIST handwritten digit database. http:\/\/yann.lecun.com\/exdb\/mnist\/. (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/","key":"e_1_3_2_1_21_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.5555\/3130379.3130383"},{"key":"e_1_3_2_1_23_1","first-page":"1","article-title":"Dynamics of some single flux quantum devices: I. Parametric quantron","volume":"13","author":"Likharev K.","year":"1977","unstructured":"K. Likharev. 1977. Dynamics of some single flux quantum devices: I. Parametric quantron. IEEE Transactions on Magnetics 13, 1 (January 1977), 242--244.","journal-title":"IEEE Transactions on Magnetics"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1109\/77.80745"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1109\/ICPP.1993.23"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1109\/TMAG.1985.1063734"},{"volume-title":"Handbook of Computational Statistics","author":"L\u00e2\u0102 Pierre","unstructured":"Pierre L\u00e2\u0102ŹEcuyer. 2012. Random number generation. In Handbook of Computational Statistics. Springer, 35--71.","key":"e_1_3_2_1_27_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1109\/HPCA.2016.7446050"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1109\/ISSCC.2017.7870353"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1109\/77.403086"},{"volume-title":"2016 Appl. Superconductivity Conference (ASC2016)","author":"Narama T.","unstructured":"T. Narama, F. China, N. Takeuchi, T. Ortlepp, Y. Yamanashi, and N. Yoshikawa. 2016. Yield evaluation of 83k-junction adiabatic-quantum-flux-parametron circuit. In 2016 Appl. Superconductivity Conference (ASC2016).","key":"e_1_3_2_1_31_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.5555\/130653"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1145\/2847263.2847265"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_34_1","DOI":"10.1145\/3007787.3001165"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_35_1","DOI":"10.1145\/3037697.3037746"},{"key":"e_1_3_2_1_36_1","volume-title":"IEEE International Conference on. IEEE, 1--7.","author":"Ren Ao","year":"2016","unstructured":"Ao Ren, Zhe Li, Yanzhi Wang, Qinru Qiu, and Bo Yuan. 2016. Designing reconfigurable large-scale deep learning systems using stochastic computing. In Rebooting Computing (ICRC), IEEE International Conference on. IEEE, 1--7."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_37_1","DOI":"10.5555\/3195638.3195659"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_38_1","DOI":"10.1145\/3061639.3062290"},{"key":"e_1_3_2_1_39_1","volume-title":"Solid-State Circuits Conference (ISSCC)","author":"Sim Jaehyeong","year":"2016","unstructured":"Jaehyeong Sim, Jun-Seok Park, Minhye Kim, Dongmyung Bae, Yeongjae Choi, and Lee-Sup Kim. 2016. 14.6 a 1.42 tops\/w deep convolutional neural network recognition processor for intelligent ioe systems. In Solid-State Circuits Conference (ISSCC), 2016 IEEE International. IEEE, 264--265."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_40_1","DOI":"10.1109\/HPCA.2018.00018"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_41_1","DOI":"10.1145\/2847263.2847276"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.1088\/1361-6668\/aa52f3"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_43_1","DOI":"10.1088\/0953-2048\/26\/3\/035010"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_44_1","DOI":"10.1063\/1.4790276"},{"key":"e_1_3_2_1_45_1","first-page":"1","article-title":"Energy efficiency of adiabatic superconductor logic","volume":"28","author":"Takeuchi Naoki","year":"2014","unstructured":"Naoki Takeuchi, Yuki Yamanashi, and Nobuyuki Yoshikawa. 2014. Energy efficiency of adiabatic superconductor logic. Superconductor Science and Technology 28, 1 (nov 2014), 015003.","journal-title":"Superconductor Science and Technology"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_46_1","DOI":"10.1063\/1.4919838"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_47_1","DOI":"10.1109\/TASC.2016.2519388"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_48_1","DOI":"10.1145\/3020078.3021744"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_49_1","DOI":"10.1145\/3079856.3080244"},{"key":"e_1_3_2_1_50_1","volume-title":"On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks. arXiv preprint arXiv:1803.05391","author":"Wang Yanzhi","year":"2018","unstructured":"Yanzhi Wang, Zheng Zhan, Jiayu Li, Jian Tang, Bo Yuan, Liang Zhao, Wujie Wen, Siyue Wang, and Xue Lin. 2018. On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks. arXiv preprint arXiv:1803.05391 (2018)."},{"key":"e_1_3_2_1_51_1","volume-title":"Solid-State Circuits Conference (ISSCC)","author":"Whatmough Paul N","year":"2017","unstructured":"Paul N Whatmough, Sae Kyu Lee, Hyunkwang Lee, Saketh Rama, David Brooks, and Gu-Yeon Wei. 2017. 14.3 A 28nm SoC with a 1.2 GHz 568nJ\/prediction sparse deep-neural-network engine with> 0.1 timing error rate tolerance for IoT applications. In Solid-State Circuits Conference (ISSCC), 2017 IEEE International. IEEE, 242--243."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_52_1","DOI":"10.5555\/1893088"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_53_1","DOI":"10.1145\/2966986.2967011"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_54_1","DOI":"10.1145\/2934583.2934644"},{"doi-asserted-by":"publisher","unstructured":"Ritchie Zhao Weinan Song Wentao Zhang Tianwei Xing Jeng-Hau Lin Mani B Srivastava Rajesh Gupta and Zhiru Zhang. 2017. Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs.. In FPGA. 15--24. 10.1145\/3020078.3021741","key":"e_1_3_2_1_55_1","DOI":"10.1145\/3020078.3021741"}],"event":{"sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","IEEE-CS\\DATC IEEE Computer Society"],"acronym":"ISCA '19","name":"ISCA '19: The 46th Annual International Symposium on Computer Architecture","location":"Phoenix Arizona"},"container-title":["Proceedings of the 46th International Symposium on Computer Architecture"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3307650.3322270","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3307650.3322270","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:06Z","timestamp":1750204446000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3307650.3322270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,22]]},"references-count":55,"alternative-id":["10.1145\/3307650.3322270","10.1145\/3307650"],"URL":"https:\/\/doi.org\/10.1145\/3307650.3322270","relation":{},"subject":[],"published":{"date-parts":[[2019,6,22]]},"assertion":[{"value":"2019-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}