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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:35:44Z","timestamp":1740184544938,"version":"3.37.3"},"reference-count":21,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"tdm","delay-in-days":14,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Electronic Components and Systems for European Leadership","award":["826655"]},{"DOI":"10.13039\/100006541","name":"Scientific Education and Research Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006541","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013348","name":"Innosuisse - Schweizerische Agentur f\u00fcr Innovationsf\u00f6rderung","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013348","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100019457","name":"Key Digital Technologies Joint Undertaking","doi-asserted-by":"crossref","award":["101097300"],"id":[{"id":"10.13039\/100019457","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Neuromorph. Comput. Eng."],"published-print":{"date-parts":[[2024,3,1]]},"abstract":"Abstract<\/jats:title>\n Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within spiking neural networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, as well as unintended parameter and dynamical variations of analog circuits due to fabrication non-idealities. Here we demonstrate a novel methodology for offline training and deployment of SNNs to the mixed-signal neuromorphic processor DYNAP-SE2. Our methodology applies gradient-based training to a differentiable simulation of the mixed-signal device, coupled with an unsupervised weight quantization method to optimize the network\u2019s parameters. Parameter noise injection during training provides robustness to the effects of quantization and device mismatch, making the method a promising candidate for real-world applications under hardware constraints and non-idealities. This work extends Rockpool, an open-source deep-learning library for SNNs, with support for accurate simulation of mixed-signal SNN dynamics. Our approach simplifies the development and deployment process for the neuromorphic community, making mixed-signal neuromorphic processors more accessible to researchers and developers.<\/jats:p>","DOI":"10.1088\/2634-4386\/ad2ec3","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T22:23:49Z","timestamp":1709245429000},"page":"014011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Gradient-descent hardware-aware training and deployment for mixed-signal neuromorphic processors"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1025-0903","authenticated-orcid":false,"given":"Ugurcan","family":"Cakal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9706-5989","authenticated-orcid":false,"family":"Maryada","sequence":"additional","affiliation":[]},{"given":"Chenxi","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0863-7116","authenticated-orcid":false,"given":"Ilkay","family":"Ulusoy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3856-826X","authenticated-orcid":true,"given":"Dylan Richard","family":"Muir","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"article-title":"Dynap-se2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor","year":"2023","author":"Richter","key":"ncead2ec3bib1"},{"key":"ncead2ec3bib2","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TBCAS.2019.2953001","article-title":"Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor","volume":"13","author":"Bauer","year":"2019","journal-title":"IEEE Trans. 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