  Spiking neural networks (SNNs) have gained considerable interest due to their
energy-efficient characteristics, yet lack of a scalable training algorithm has
restricted their applicability in practical machine learning problems. The deep
neural network-to-SNN conversion approach has been widely studied to broaden
the applicability of SNNs. Most previous studies, however, have not fully
utilized spatio-temporal aspects of SNNs, which has led to inefficiency in
terms of number of spikes and inference latency. In this paper, we present
T2FSNN, which introduces the concept of time-to-first-spike coding into deep
SNNs using the kernel-based dynamic threshold and dendrite to overcome the
aforementioned drawback. In addition, we propose gradient-based optimization
and early firing methods to further increase the efficiency of the T2FSNN.
According to our results, the proposed methods can reduce inference latency and
number of spikes to 22% and less than 1%, compared to those of burst coding,
which is the state-of-the-art result on the CIFAR-100.
