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Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
Yifan Hu, Yujie Wu, Lei Deng +1
Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict t…
Advancing Spiking Neural Networks towards Deep Residual Learning
Yifan Hu, Lei Deng, Yujie Wu +2
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their applicatio…
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Ling Liang, Zheng Qu, Zhaodong Chen +6
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits thei…
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Mingkun Xu, Yujie Wu, Lei Deng +3
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex envi…
Going Deeper With Directly-Trained Larger Spiking Neural Networks
Hanle Zheng, Yujie Wu, Lei Deng +2
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient…
Brain-inspired global-local learning incorporated with neuromorphic computing
Yujie Wu, Rong Zhao, Jun Zhu +11
Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide…
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Ling Liang, Xing Hu, Lei Deng +5
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models ac…
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Lei Deng, Yujie Wu, Yifan Hu +6
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with h…
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Jibin Wu, Yansong Chua, Malu Zhang +3
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentia…
Deep Spiking Neural Network with Spike Count based Learning Rule
Jibin Wu, Yansong Chua, Malu Zhang +3
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synch…
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Yujie Wu, Lei Deng, Guoqi Li +2
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive p…
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Yujie Wu, Lei Deng, Guoqi Li +2
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal…