{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T20:23:49Z","timestamp":1793305429404,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,4]]},"DOI":"10.1145\/3822454.3822481","type":"proceedings-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:57:39Z","timestamp":1784181459000},"page":"8-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1060-8916","authenticated-orcid":false,"given":"Hyeongmeen","family":"Baik","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8599-4321","authenticated-orcid":false,"given":"Hamed","family":"Poursiami","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4855-4593","authenticated-orcid":false,"given":"Maryam","family":"Parsa","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1446-0417","authenticated-orcid":false,"given":"Jinia","family":"Roy","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3320288.3320304"},{"key":"e_1_3_3_1_3_2","unstructured":"BrainChip Holdings. 2022. Akida Neuromorphic Processor: Product Brief. https:\/\/brainchip.com\/akida-neural-processor-soc\/."},{"key":"e_1_3_3_1_4_2","unstructured":"Ricky\u00a0TQ Chen Yulia Rubanova Jesse Bettencourt and David\u00a0K Duvenaud. 2018. Neural ordinary differential equations. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10191614"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Mike Davies Narayan Srinivasa Tsung-Han Lin Gautham Chinya Yongqiang Cao Sri\u00a0Harsha Choday Georgios Dimou Prasad Joshi Nabil Imam Shweta Jain et\u00a0al. 2018. Loihi: A neuromorphic manycore processor with on-chip learning. Ieee micro 38 1 (2018) 82\u201399.","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Mike Davies Andreas Wild Garrick Orchard Yulia Sandamirskaya Gabriel A\u00a0Fonseca Guerra Prasad Joshi Philipp Plank and Sumedh\u00a0R Risbud. 2021. Advancing neuromorphic computing with loihi: A survey of results and outlook. Proc. IEEE 109 5 (2021) 911\u2013934.","DOI":"10.1109\/JPROC.2021.3067593"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Jason\u00a0K Eshraghian Max Ward Emre\u00a0O Neftci Xinxin Wang Gregor Lenz Girish Dwivedi Mohammed Bennamoun Doo\u00a0Seok Jeong and Wei\u00a0D Lu. 2023. Training spiking neural networks using lessons from deep learning. Proc. IEEE 111 9 (2023) 1016\u20131054.","DOI":"10.1109\/JPROC.2023.3308088"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Youssof Fassi Vincent Heiries Jerome Boutet and Sebastien Boisseau. 2023. Toward physics-informed machine-learning-based predictive maintenance for power converters\u2014a review. IEEE Transactions on Power Electronics 39 2 (2023) 2692\u20132720.","DOI":"10.1109\/TPEL.2023.3328438"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Alexander Henkes Jason\u00a0K Eshraghian and Henning Wessels. 2024. Spiking neural networks for nonlinear regression. Royal Society Open Science 11 5 (2024).","DOI":"10.1098\/rsos.231606"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Dhireesha Kudithipudi Catherine Schuman Craig\u00a0M Vineyard Tej Pandit Cory Merkel Rajkumar Kubendran James\u00a0B Aimone Garrick Orchard Christian Mayr Ryad Benosman et\u00a0al. 2025. Neuromorphic computing at scale. Nature 637 8047 (2025) 801\u2013812.","DOI":"10.1038\/s41586-024-08253-8"},{"key":"e_1_3_3_1_12_2","unstructured":"Liangzhen Lai Naveen Suda and Vikas Chandra. 2018. Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1801.06601 (2018)."},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Congyang Liu Ziyi Yang Xin Zhang Zikai Zhu Haoming Chu Yuxiang Huan Li-Rong Zheng and Zhuo Zou. 2023. A low-power hybrid-precision neuromorphic processor with INT8 inference and INT16 online learning in 40-nm CMOS. IEEE Transactions on Circuits and Systems I: Regular Papers 70 10 (2023) 4028\u20134039.","DOI":"10.1109\/TCSI.2023.3300095"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Wolfgang Maass. 1997. Networks of spiking neurons: the third generation of neural network models. Neural networks 10 9 (1997) 1659\u20131671.","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Emre\u00a0O Neftci Hesham Mostafa and Friedemann Zenke. 2019. Surrogate gradient learning in spiking neural networks. IEEE Signal Processing Magazine 36 6 (2019) 51\u201363.","DOI":"10.1109\/MSP.2019.2931595"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Maziar Raissi Paris Perdikaris and George\u00a0E Karniadakis. 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics 378 (2019) 686\u2013707.","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Kaushik Roy Akhilesh Jaiswal and Priyadarshini Panda. 2019. Towards spike-based machine intelligence with neuromorphic computing. Nature 575 7784 (2019) 607\u2013617.","DOI":"10.1038\/s41586-019-1677-2"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.36001\/phmconf.2023.v15i1.3494"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICONS69015.2025.00034"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Penghao Wu Engang Tian Hongfeng Tao and Yiyang Chen. 2025. Data-driven spiking neural networks for intelligent fault detection in vehicle lithium-ion battery systems. Engineering Applications of Artificial Intelligence 141 (2025) 109756.","DOI":"10.1016\/j.engappai.2024.109756"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Yangxiao Xiang Hongjian Lin and Henry Shu-Hung Chung. 2024. Extended physics-informed neural networks for parameter identification of switched mode power converters with undetermined topological durations. IEEE Transactions on Power Electronics 40 1 (2024) 2235\u20132247.","DOI":"10.1109\/TPEL.2024.3481158"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Friedemann Zenke and Tim\u00a0P Vogels. 2021. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural computation 33 4 (2021) 899\u2013925.","DOI":"10.1162\/neco_a_01367"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Shuai Zhao Yingzhou Peng Yi Zhang and Huai Wang. 2022. Parameter estimation of power electronic converters with physics-informed machine learning. IEEE Transactions on Power Electronics 37 10 (2022) 11567\u201311578.","DOI":"10.1109\/TPEL.2022.3176468"}],"event":{"name":"ICONS '26: International Conference on Neuromorphic Systems","location":"Chicago USA","acronym":"ICONS 2026","sponsor":["SIGDA ACM Special Interest Group on Design Automation","SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Proceedings of the International Conference on Neuromorphic Systems"],"original-title":[],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:58:23Z","timestamp":1784181503000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3822454.3822481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":22,"alternative-id":["10.1145\/3822454.3822481","10.1145\/3822454"],"URL":"https:\/\/doi.org\/10.1145\/3822454.3822481","relation":{},"subject":[],"published":{"date-parts":[[2026,8,4]]},"assertion":[{"value":"2026-08-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}