Noise Adaptor in Spiking Neural Networks
Chen Li Bipin Rajendran
Recent strides in low-latency spiking neural network (SNN) algorithms have
drawn significant interest, particularly due to their event-driven computing
nature and fast inference capability. One of the most efficient ways to
construct a low-latency SNN is by converting a pre-trained, low-bit artificial
neural network (ANN) into an SNN. However, this conversion process faces two
main challenges: First, converting SNNs from low-bit ANNs can lead to
``occasional noise" -- the phenomenon where occasional spikes are generated in
spiking neurons where they should not be -- during inference, which
significantly lowers SNN accuracy. Second, although low-latency SNNs initially
show fast improvements in accuracy with time steps, these accuracy growths soon
plateau, resulting in their peak accuracy lagging behind both full-precision
ANNs and traditional ``long-latency SNNs'' that prioritize precision over
speed.
  In response to these two challenges, this paper introduces a novel technique
named ``noise adaptor.'' Noise adaptor can model occasional noise during
training and implicitly optimize SNN accuracy, particularly at high simulation
times $T$. Our research utilizes the ResNet model for a comprehensive analysis
of the impact of the noise adaptor on low-latency SNNs. The results demonstrate
that our method outperforms the previously reported quant-ANN-to-SNN conversion
technique. We achieved an accuracy of 95.95\% within 4 time steps on CIFAR-10
using ResNet-18, and an accuracy of 74.37\% within 64 time steps on ImageNet
using ResNet-50. Remarkably, these results were obtained without resorting to
any noise correction methods during SNN inference, such as negative spikes or
two-stage SNN simulations. Our approach significantly boosts the peak accuracy
of low-latency SNNs, bringing them on par with the accuracy of full-precision
ANNs. Code will be open source.