  Spiking neural networks (SNNs) are energy-efficient neural networks because
of their spiking nature. However, as the spike firing rate of SNNs increases,
the energy consumption does as well, and thus, the advantage of SNNs
diminishes. Here, we tackle this problem by introducing a novel penalty term
for the spiking activity into the objective function in the training phase. Our
method is designed so as to optimize the energy consumption metric directly
without modifying the network architecture. Therefore, the proposed method can
reduce the energy consumption more than other methods while maintaining the
accuracy. We conducted experiments for image classification tasks, and the
results indicate the effectiveness of the proposed method, which mitigates the
dilemma of the energy--accuracy trade-off.
