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.

翻译：脉冲神经网络（SNNs）因其脉冲驱动特性而具有节能优势。然而，随着SNN的脉冲发放率升高，其能耗也随之增加，导致这一优势逐渐减弱。本文通过在训练阶段的目标函数中引入新颖的脉冲活动惩罚项来解决该问题。所提方法无需修改网络架构即可直接优化能耗指标。因此，相较于其他方法，本方法能在保持精度的同时更大程度降低能耗。我们在图像分类任务上开展了实验，结果表明该方法能有效缓解能耗-精度权衡困境。