Spiking Neural Networks (SNNs) have recently attracted widespread research
interest as an efficient alternative to traditional Artificial Neural Networks
(ANNs) because of their capability to process sparse and binary spike
information and avoid expensive multiplication operations. Although the
efficiency of SNNs can be realized on the In-Memory Computing (IMC)
architecture, we show that the energy cost and latency of SNNs scale linearly
with the number of timesteps used on IMC hardware. Therefore, in order to
maximize the efficiency of SNNs, we propose input-aware Dynamic Timestep SNN
(DT-SNN), a novel algorithmic solution to dynamically determine the number of
timesteps during inference on an input-dependent basis. By calculating the
entropy of the accumulated output after each timestep, we can compare it to a
predefined threshold and decide if the information processed at the current
timestep is sufficient for a confident prediction. We deploy DT-SNN on an IMC
architecture and show that it incurs negligible computational overhead. We
demonstrate that our method only uses 1.46 average timesteps to achieve the
accuracy of a 4-timestep static SNN while reducing the energy-delay-product by
80%.

当前，脉冲神经网络因其对稀疏和二元脉冲信息的处理能力以及避免昂贵的乘法操作而引起了广泛研究兴趣。然而，如果使用 In-Memory Computing（IMC）架构，SNNs 的能量成本和延迟会随时间步数线性增长。因此，为了最大化 SNN 的效率，我们提出了一种基于动态时间步长的输入感知 SNN 算法，通过计算每个时间步长后输出的熵来动态确定时间步长的数量，从而实现将能量延迟产品降低 80%。