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Given that the number of spikes (rather than timesteps) is the major cause of inference delay for digital neuromorphic hardware, NOSONets trained using BPLC likely reduce inference delay significantly. To identify the feasibility of BPLC\u00a0+\u00a0NOSO, we trained CNN-based NOSONets on Fashion-MNIST and CIFAR-10. The classification accuracy on CIFAR-10 exceeds the state-of-the-art result from an SNN of the same depth and width by approximately 2%. Additionally, the number of spikes for inference is significantly reduced (by approximately one order of magnitude), highlighting a significant reduction in inference delay.<\/jats:p>","DOI":"10.1007\/s40747-023-00983-y","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T08:06:21Z","timestamp":1677225981000},"page":"4959-4976","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BPLC + NOSO: backpropagation of errors based on latency code with neurons that only spike once at most"],"prefix":"10.1007","volume":"9","author":[{"given":"Seong Min","family":"Jin","sequence":"first","affiliation":[]},{"given":"Dohun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Dong Hyung","family":"Yoo","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Eshraghian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7954-2213","authenticated-orcid":false,"given":"Doo Seok","family":"Jeong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"983_CR1","doi-asserted-by":"publisher","unstructured":"Amir A, Taba B, Berg D, et\u00a0al (2017) A low power, fully event-based gesture recognition system. 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