Spiking neural networks (SNNs) have advantages in latency and energy
efficiency over traditional artificial neural networks (ANNs) due to its
event-driven computation mechanism and replacement of energy-consuming weight
multiplications with additions. However, in order to reach accuracy of its ANN
counterpart, it usually requires long spike trains to ensure the accuracy.
Traditionally, a spike train needs around one thousand time steps to approach
similar accuracy as its ANN counterpart. This offsets the computation
efficiency brought by SNNs because longer spike trains mean a larger number of
operations and longer latency. In this paper, we propose a radix encoded SNN
with ultra-short spike trains. In the new model, the spike train takes less
than ten time steps. Experiments show that our method demonstrates 25X speedup
and 1.1% increment on accuracy, compared with the state-of-the-art work on
VGG-16 network architecture and CIFAR-10 dataset