# Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks Source: [Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:03.649Z Authors: Kazuma Suetake, Takuya Ushimaru, Ryuji Saiin, Yoshihide Sawada Published: 2023-02-03T02:30:00.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()