{"type":"ScholarlyArticle","title":"Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks","authors":[{"name":"Yuchen Wang","slug":"yuchen-wang","given":"Yuchen","family":"Wang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Kexin Shi","slug":"kexin-shi","given":"Kexin","family":"Shi","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Chengzhuo Lu","slug":"chengzhuo-lu","given":"Chengzhuo","family":"Lu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Yuguo Liu","slug":"yuguo-liu","given":"Yuguo","family":"Liu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Malu Zhang","slug":"malu-zhang","given":"Malu","family":"Zhang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Hong Qu","slug":"hong-qu","given":"Hong","family":"Qu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"}],"abstract":"The brain-inspired spiking neural networks (SNNs) are receiving increasing attention due to their asynchronous event-driven characteristics and low power consumption. As attention mechanisms recently become an indispensable part of sequence dependence modeling, the combination of SNNs and attention mechanisms holds great potential for energy-efficient and high-performance computing paradigms. However, the existing works cannot benefit from both temporal-wise attention and the asynchronous characteristic of SNNs. To fully leverage the advantages of both SNNs and attention mechanisms, we propose an SNNs-based spatial-temporal self-attention (STSA) mechanism, which calculates the feature dependence across the time and space domains without destroying the asynchronous transmission properties of SNNs. To further improve the performance, we also propose a spatial-temporal relative position bias (STRPB) for STSA to consider the spatiotemporal position of spikes. Based on the STSA and STRPB, we construct a spatial-temporal spiking Transformer framework, named STS-Transformer, which is powerful and enables SNNs to work in an asynchronous event-driven manner. Extensive experiments are conducted on popular neuromorphic datasets and speech datasets, including DVS128 Gesture, CIFAR10-DVS, and Google Speech Commands, and our experimental results can outperform other state-of-the-art models.","doi":"10.24963/ijcai.2023/344","url":"https://ijcai.org/proceedings/2023/344","pdf":"https://ijcai.org/proceedings/2023/0344.pdf","markdown":"https://ijcai.org/proceedings/2023/344.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/344","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Humans and AI","track":"Main Track","number":344,"pages":{"first":3085,"last":3093},"keywords":["Humans and AI: HAI: Cognitive modeling","Humans and AI: HAI: Cognitive systems","Humans and AI: HAI: Applications"],"videos":[],"references":[{"position":1,"raw":"[Amir et al., 2017] Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Gar- reau, Marcela Mendoza, et al. 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