16 citations · 88 across the 24 of their papers we have counts for
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cs.NE2023★ 4 cited
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Shen Yan +3
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (B…
cs.NE2022★ 11 cited
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
Qingyan Meng, Mingqing Xiao, Shen Yan +3
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 n…