{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/going-deeper-in-spiking-neural-networks-vgg","title":"Going Deeper in Spiking Neural Networks: VGG and Residual Architectures","arxiv_id":"1802.02627","date":"2018-02-07","proceeding":null,"authors":["Abhronil Sengupta","Yuting Ye","Robert Wang","Chiao Liu","Kaushik Roy"],"abstract":"Over the past few years, Spiking Neural Networks (SNNs) have become popular\nas a possible pathway to enable low-power event-driven neuromorphic hardware.\nHowever, their application in machine learning have largely been limited to\nvery shallow neural network architectures for simple problems. In this paper,\nwe propose a novel algorithmic technique for generating an SNN with a deep\narchitecture, and demonstrate its effectiveness on complex visual recognition\nproblems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and\nResidual network architectures, with significantly better accuracy than the\nstate-of-the-art. Finally, we present analysis of the sparse event-driven\ncomputations to demonstrate reduced hardware overhead when operating in the\nspiking domain.","url_abs":"http://arxiv.org/abs/1802.02627v4","url_pdf":"http://arxiv.org/pdf/1802.02627v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"going-deeper-in-spiking-neural-networks-vgg","repo_url":"https://github.com/emi-group/mixer-snn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.02627","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}