  Spiking neural networks (SNNs) are shown to be more biologically plausible
and energy efficient over their predecessors. However, there is a lack of an
efficient and generalized training method for deep SNNs, especially for
deployment on analog computing substrates. In this paper, we put forward a
generalized learning rule, termed Local Tandem Learning (LTL). The LTL rule
follows the teacher-student learning approach by mimicking the intermediate
feature representations of a pre-trained ANN. By decoupling the learning of
network layers and leveraging highly informative supervisor signals, we
demonstrate rapid network convergence within five training epochs on the
CIFAR-10 dataset while having low computational complexity. Our experimental
results have also shown that the SNNs thus trained can achieve comparable
accuracies to their teacher ANNs on CIFAR-10, CIFAR-100, and Tiny ImageNet
datasets. Moreover, the proposed LTL rule is hardware friendly. It can be
easily implemented on-chip to perform fast parameter calibration and provide
robustness against the notorious device non-ideality issues. It, therefore,
opens up a myriad of opportunities for training and deployment of SNN on
ultra-low-power mixed-signal neuromorphic computing chips.10
