DTS-SNN: Spiking Neural Networks with Dynamic Time-Surfaces

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Yoo, Donghyung, Jeong, Doo Seok
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Classification; Convolution; Convolutional neural networks; Datasets; dynamic time-surfaces; Efficiency; Encoding; Event detection; event-based data; Feature extraction; Lightweight spiking neural network; Neural networks; Neuromorphics; Spiking; spiking neural network; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3209671

Convolution helps spiking neural networks (SNNs) capture the spatio-temporal structures of neuromorphic (event) data as evident in the convolution-based SNNs (C-SNNs) with the state-of-the-art classification-accuracies on various datasets. However, the efficacy aside, the efficiency of C-SNN is questionable. In this regard, we propose SNNs with novel trainable dynamic time-surfaces (DTS-SNNs) as efficient alternatives to convolution. The novel dynamic time-surface proposed in this work features its high responsiveness to moving objects given the use of the zero-sum temporal kernel that is motivated by the simple cells' receptive fields in the early stage visual pathway. We evaluated the performance and computational complexity of our DTS-SNNs on three real-world event-based datasets (DVS128 Gesture, Spiking Heidelberg dataset, N-Cars). The results highlight high classification accuracies and significant improvements in computational efficiency, e.g., merely 1.51% behind of the state-of-the-art result on DVS128 Gesture but a × 18 improvement in efficiency.