{"type":"ScholarlyArticle","title":"LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization","authors":[{"name":"Qianhui Liu","slug":"qianhui-liu","given":"Qianhui","family":"Liu","affiliation":"National Univerisity of Singapore , Singapore"},{"name":"Jiaqi Yan","slug":"jiaqi-yan","given":"Jiaqi","family":"Yan","affiliation":"Zhejiang University , China"},{"name":"Malu Zhang","slug":"malu-zhang","given":"Malu","family":"Zhang","affiliation":"University of Electronic Science and Technology of China , China"},{"name":"Gang Pan","slug":"gang-pan","given":"Gang","family":"Pan","affiliation":"Zhejiang University , China"},{"name":"Haizhou Li","slug":"haizhou-li","given":"Haizhou","family":"Li","affiliation":"The Chinese University of Hong Kong , Shenzhen (CUHK-Shenzhen) , China; Shenzhen Research Institute of Big Data , China; National Univerisity of Singapore , Singapore"}],"abstract":"Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource constraints of these devices. In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. Spatially, we present a novel Compressive Convolution block (CompConv) to expand the search space to support pruning and mixed-precision quantization. Temporally, we are the first to propose a compressive timestep search to identify the optimal number of timesteps under specific computation cost constraints. Finally, we formulate a joint optimization to simultaneously learn the architecture parameters and spatial-temporal compression strategies to achieve high performance while minimizing memory and computation costs. Experimental results on CIFAR-10, CIFAR-100, and Google Speech Command datasets demonstrate our proposed LitE-SNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs.","doi":"10.24963/ijcai.2024/343","url":"https://ijcai.org/proceedings/2024/343","pdf":"https://ijcai.org/proceedings/2024/0343.pdf","markdown":"https://ijcai.org/proceedings/2024/343.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/343","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Humans and AI","track":"Main Track","number":343,"pages":{"first":3097,"last":3105},"keywords":["Humans and AI: HAI: Cognitive modeling","Humans and AI: HAI: Cognitive systems"],"videos":[],"references":[{"position":1,"raw":"[Cai and Vasconcelos, 2020] Zhaowei Cai and Nuno Vas- concelos. 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