Joint a-snn: Joint training of artificial and spiking neural networks via self-distillation and weight factorization
Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking
nature of brain neurons and have received lots of research attention. SNNs deal with binary …
nature of brain neurons and have received lots of research attention. SNNs deal with binary …
HybridSNN: Combining bio-machine strengths by boosting adaptive spiking neural networks
Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide
biologically relevant and low-power consuming models for information processing. Existing …
biologically relevant and low-power consuming models for information processing. Existing …
Differentiable spike: Rethinking gradient-descent for training spiking neural networks
Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
Temporal efficient training of spiking neural network via gradient re-weighting
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
Optimal ann-snn conversion for fast and accurate inference in deep spiking neural networks
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …
attracted great attentions from researchers and industry. The most efficient way to train deep …
[HTML][HTML] S3NN: Time step reduction of spiking surrogate gradients for training energy efficient single-step spiking neural networks
As the scales of neural networks increase, techniques that enable them to run with low
computational cost and energy efficiency are required. From such demands, various efficient …
computational cost and energy efficiency are required. From such demands, various efficient …
Constructing accurate and efficient deep spiking neural networks with double-threshold and augmented schemes
Spiking neural networks (SNNs) are considered as a potential candidate to overcome
current challenges, such as the high-power consumption encountered by artificial neural …
current challenges, such as the high-power consumption encountered by artificial neural …
Real spike: Learning real-valued spikes for spiking neural networks
Y Guo, L Zhang, Y Chen, X Tong, X Liu… - … on Computer Vision, 2022 - Springer
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …
attention due to their event-driven and energy-efficient characteristics. The integration of …
A tandem learning rule for effective training and rapid inference of deep spiking neural networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …
computing model for neuromorphic computing (NC) architectures. However, due to the …
Spikeformer: a novel architecture for training high-performance low-latency spiking neural network
Spiking neural networks (SNNs) have made great progress on both performance and
efficiency over the last few years, but their unique working pattern makes it hard to train a …
efficiency over the last few years, but their unique working pattern makes it hard to train a …
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