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pro vyhledávání: '"XIAO MingQing"'
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biologically plausible and neuromorphic-hardware-friendly way is still cha
Externí odkaz:
http://arxiv.org/abs/2407.12516
Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation.
Externí odkaz:
http://arxiv.org/abs/2405.16851
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from cat
Externí odkaz:
http://arxiv.org/abs/2402.11984
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (BPTT) with surrogate gradients (SG) method has achieved high performanc
Externí odkaz:
http://arxiv.org/abs/2302.14311
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural netwo
Externí odkaz:
http://arxiv.org/abs/2302.00232
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with su
Externí odkaz:
http://arxiv.org/abs/2210.04195
Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recov
Externí odkaz:
http://arxiv.org/abs/2210.04188
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiability. Most existing methods either suffer from high la
Externí odkaz:
http://arxiv.org/abs/2205.00459
Autor:
Wu, Jiaming, Xiao, Mingqing, Dai, Linfabao, Bo, Huajun, Lian, Zhixiang, Zhou, Hao, Yang, Jian, Pu, Jianwei, Cheng, Hongzhan
Publikováno v:
In Computers and Geotechnics September 2024 173
