{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:00:18Z","timestamp":1750309218267,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T00:00:00Z","timestamp":1729987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,27]]},"DOI":"10.1145\/3676536.3676662","type":"proceedings-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:53:56Z","timestamp":1744203236000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["KirchhoffNet: A Scalable Ultra Fast Analog Neural Network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1515-4198","authenticated-orcid":false,"given":"Zhengqi","family":"Gao","sequence":"first","affiliation":[{"name":"Dept. of EECS, MIT, Cambridge, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0156-762X","authenticated-orcid":false,"given":"Fan-keng","family":"Sun","sequence":"additional","affiliation":[{"name":"Dept. of EECS, MIT, Cambridge, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7648-4932","authenticated-orcid":false,"given":"Ron","family":"Rohrer","sequence":"additional","affiliation":[{"name":"Dept. of ECE, CMU, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-445X","authenticated-orcid":false,"given":"Duane S.","family":"Boning","sequence":"additional","affiliation":[{"name":"Dept. of EECS, MIT, Cambridge, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Quantum machine learning. Nature 549, 7671","author":"Biamonte Jacob","year":"2017","unstructured":"Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning. Nature 549, 7671 (2017), 195--202."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3304103"},{"key":"e_1_3_2_1_3_1","first-page":"89","article-title":"Neuromorphic computing using non-volatile memory","author":"Burr Geoffrey W","year":"2017","unstructured":"Geoffrey W Burr, Robert M Shelby, Abu Sebastian, Sangbum Kim, Seyoung Kim, Severin Sidler, Kumar Virwani, Masatoshi Ishii, Pritish Narayanan, Alessandro Fumarola, et al. 2017. Neuromorphic computing using non-volatile memory. Advances in Physics: X 2, 1 (2017), 89--124.","journal-title":"Advances in Physics"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"e_1_3_2_1_5_1","volume-title":"Neural ordinary differential equations. Advances in Neural Information Processing Systems 31","author":"Chen Ricky TQ","year":"2018","unstructured":"Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural ordinary differential equations. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_6_1","first-page":"10443","article-title":"Learning discrete energy-based models via auxiliary-variable local exploration","volume":"33","author":"Dai Hanjun","year":"2020","unstructured":"Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, and Dale Schuurmans. 2020. Learning discrete energy-based models via auxiliary-variable local exploration. Advances in Neural Information Processing Systems 33 (2020), 10443--10455.","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"Basic Circuit Theory","author":"Desoer Charles A","key":"e_1_3_2_1_7_1","unstructured":"Charles A Desoer and Ernest S Kuh. 1969. Basic Circuit Theory. McGraw-Hill."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.18.014040"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCT.1969.1082965"},{"key":"e_1_3_2_1_10_1","volume-title":"Augmented neural ODEs. Advances in Neural Information Processing Systems 32","author":"Dupont Emilien","year":"2019","unstructured":"Emilien Dupont, Arnaud Doucet, and Yee Whye Teh. 2019. Augmented neural ODEs. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_11_1","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi Vijay Prakash","year":"2023","unstructured":"Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2023. Benchmarking graph neural networks. Journal of Machine Learning Research 24, 43 (2023), 1--48.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2947008"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.089"},{"key":"e_1_3_2_1_14_1","volume-title":"Advances in quantum deep learning: An overview. arXiv preprint arXiv:2005.04316","author":"Garg Siddhant","year":"2020","unstructured":"Siddhant Garg and Goutham Ramakrishnan. 2020. Advances in quantum deep learning: An overview. arXiv preprint arXiv:2005.04316 (2020)."},{"volume-title":"Ferroelectric FET analog synapse for acceleration of deep neural network training. In 2017 IEEE international electron devices meeting (IEDM)","author":"Jerry Matthew","key":"e_1_3_2_1_15_1","unstructured":"Matthew Jerry, Pai-Yu Chen, Jianchi Zhang, Pankaj Sharma, Kai Ni, Shimeng Yu, and Suman Datta. 2017. Ferroelectric FET analog synapse for acceleration of deep neural network training. In 2017 IEEE international electron devices meeting (IEDM). IEEE, 6--2."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2018.8297385"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394885.3431629"},{"key":"e_1_3_2_1_18_1","unstructured":"Jack Kendall. 2021. A Gradient Estimator for Time-Varying Electrical Networks with Non-Linear Dissipation. arXiv:2103.05636 [cs.LG]"},{"key":"e_1_3_2_1_19_1","unstructured":"Jack Kendall Ross Pantone Kalpana Manickavasagam Yoshua Bengio and Benjamin Scellier. 2020. Training End-to-End Analog Neural Networks with Equilibrium Propagation. arXiv:2006.01981 [cs.NE]"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/31.1783"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2744769.2744870"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3260163"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aat8084"},{"key":"e_1_3_2_1_24_1","volume-title":"KAN: Kolmogorov-Arnold Networks. arXiv:2404.19756 [cs.LG]","author":"Liu Ziming","year":"2024","unstructured":"Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Solja\u010di\u0107, Thomas Y. Hou, and Max Tegmark. 2024. KAN: Kolmogorov-Arnold Networks. arXiv:2404.19756 [cs.LG]"},{"key":"e_1_3_2_1_25_1","first-page":"3952","article-title":"Dissecting neural odes","volume":"33","author":"Massaroli Stefano","year":"2020","unstructured":"Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, and Hajime Asama. 2020. Dissecting neural odes. Advances in Neural Information Processing Systems 33 (2020), 3952--3963.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2010.41"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.01.013"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2008.917757"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178454"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317770"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093336.3037746"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218505"},{"key":"e_1_3_2_1_33_1","volume-title":"A deep learning theory for neural networks grounded in physics. arXiv preprint arXiv:2103.09985","author":"Scellier Benjamin","year":"2021","unstructured":"Benjamin Scellier. 2021. A deep learning theory for neural networks grounded in physics. arXiv preprint arXiv:2103.09985 (2021)."},{"key":"e_1_3_2_1_34_1","volume-title":"A Fast Algorithm to Simulate Nonlinear Resistive Networks. arXiv preprint arXiv:2402.11674","author":"Scellier Benjamin","year":"2024","unstructured":"Benjamin Scellier. 2024. A Fast Algorithm to Simulate Nonlinear Resistive Networks. arXiv preprint arXiv:2402.11674 (2024)."},{"key":"e_1_3_2_1_35_1","volume-title":"A survey of neuromorphic computing and neural networks in hardware. arXiv preprint arXiv:1705.06963","author":"Schuman Catherine D","year":"2017","unstructured":"Catherine D Schuman, Thomas E Potok, Robert M Patton, J Douglas Birdwell, Mark E Dean, Garrett S Rose, and James S Plank. 2017. A survey of neuromorphic computing and neural networks in hardware. arXiv preprint arXiv:1705.06963 (2017)."},{"key":"e_1_3_2_1_36_1","volume-title":"Riduan Khaddam-Aljameh, and Evangelos Eleftheriou.","author":"Sebastian Abu","year":"2020","unstructured":"Abu Sebastian, Manuel Le Gallo, Riduan Khaddam-Aljameh, and Evangelos Eleftheriou. 2020. Memory devices and applications for in-memory computing. Nature nanotechnology 15, 7 (2020), 529--544."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.3034856"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Yichen Shen Nicholas C Harris Scott Skirlo Mihika Prabhu Tom Baehr-Jones Michael Hochberg Xin Sun Shijie Zhao Hugo Larochelle Dirk Englund et al. 2017. Deep learning with coherent nanophotonic circuits. Nature photonics 11 7 (2017) 441--446.","DOI":"10.1038\/nphoton.2017.93"},{"key":"e_1_3_2_1_39_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"volume-title":"Network analysis","author":"Van Valkenburg Mac Elwyn","key":"e_1_3_2_1_40_1","unstructured":"Mac Elwyn Van Valkenburg. 1974. Network analysis. Prentice-Hall."},{"key":"e_1_3_2_1_41_1","first-page":"513","article-title":"DLAU: A scalable deep learning accelerator unit on FPGA","volume":"36","author":"Wang Chao","year":"2016","unstructured":"Chao Wang, Lei Gong, Qi Yu, Xi Li, Yuan Xie, and Xuehai Zhou. 2016. DLAU: A scalable deep learning accelerator unit on FPGA. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36, 3 (2016), 513--517.","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"e_1_3_2_1_42_1","volume-title":"Quantum deep learning. arXiv preprint arXiv:1412.3489","author":"Wiebe Nathan","year":"2014","unstructured":"Nathan Wiebe, Ashish Kapoor, and Krysta M Svore. 2014. Quantum deep learning. arXiv preprint arXiv:1412.3489 (2014)."},{"key":"e_1_3_2_1_43_1","volume-title":"Memristive crossbar arrays for brain-inspired computing. Nature materials 18, 4","author":"Xia Qiangfei","year":"2019","unstructured":"Qiangfei Xia and J Joshua Yang. 2019. Memristive crossbar arrays for brain-inspired computing. Nature materials 18, 4 (2019), 309--323."},{"key":"e_1_3_2_1_44_1","volume-title":"International conference on machine learning. PMLR, 38100--38124","author":"Xiao Wei","year":"2023","unstructured":"Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, and Daniela Rus. 2023. On the forward invariance of neural odes. In International conference on machine learning. PMLR, 38100--38124."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2940649"},{"key":"e_1_3_2_1_46_1","volume-title":"Compute-in-memory chips for deep learning: Recent trends and prospects","author":"Yu Shimeng","year":"2021","unstructured":"Shimeng Yu, Hongwu Jiang, Shanshi Huang, Xiaochen Peng, and Anni Lu. 2021. Compute-in-memory chips for deep learning: Recent trends and prospects. IEEE circuits and systems magazine 21, 3 (2021), 31--56."}],"event":{"name":"ICCAD '24: 43rd IEEE\/ACM International Conference on Computer-Aided Design","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CAS","IEEE CEDA","IEEE EDS"],"location":"Newark Liberty International Airport Marriott New York NY USA","acronym":"ICCAD '24"},"container-title":["Proceedings of the 43rd IEEE\/ACM International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3676536.3676662","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3676536.3676662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:43:57Z","timestamp":1750290237000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3676536.3676662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,27]]},"references-count":46,"alternative-id":["10.1145\/3676536.3676662","10.1145\/3676536"],"URL":"https:\/\/doi.org\/10.1145\/3676536.3676662","relation":{},"subject":[],"published":{"date-parts":[[2024,10,27]]},"assertion":[{"value":"2025-04-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}