Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly
used deep neural networks (DNNs). Through event-driven information processing,
SNNs can reduce the expensive compute requirements of DNNs considerably, while
achieving comparable performance. However, high inference latency is a
significant hindrance to the edge deployment of deep SNNs. Computation over
multiple timesteps not only increases latency as well as overall energy budget
due to higher number of operations, but also incurs memory access overhead of
fetching membrane potentials, both of which lessen the energy benefits of SNNs.
To overcome this bottleneck and leverage the full potential of SNNs, we propose
an Iterative Initialization and Retraining method for SNNs (IIR-SNN) to perform
single shot inference in the temporal axis. The method starts with an SNN
trained with T timesteps (T>1). Then at each stage of latency reduction, the
network trained at previous stage with higher timestep is utilized as
initialization for subsequent training with lower timestep. This acts as a
compression method, as the network is gradually shrunk in the temporal domain.
In this paper, we use direct input encoding and choose T=5, since as per
literature, it is the minimum required latency to achieve satisfactory
performance on ImageNet. The proposed scheme allows us to obtain SNNs with up
to unit latency, requiring a single forward pass during inference. We achieve
top-1 accuracy of 93.05%, 70.15% and 67.71% on CIFAR-10, CIFAR-100 and
ImageNet, respectively using VGG16, with just 1 timestep. In addition, IIR-SNNs
perform inference with 5-2500X reduced latency compared to other
state-of-the-art SNNs, maintaining comparable or even better accuracy.
Furthermore, in comparison with standard DNNs, the proposed IIR-SNNs
provide25-33X higher energy efficiency, while being comparable to them in
classification performance.

本文提出了一种针对脉冲神经网络的迭代初始化及重训练方法 (IIR-SNN) 来进行单次推断，在保证精度的情况下将时延降到了极致，实验结果表明，该方法不仅具有可比较的性能，而且比同类算法在能效方面提高了 25-33 倍，相当于标准深度神经网络的性能。