Recent animal studies have shown that biological brains can enter a low power mode in times of food scarcity. This paper explores the possibility of applying similar mechanisms to a broad class of neuromorphic systems where power consumption is strongly dependent on the magnitude of synaptic weights. In particular, we show through mathematical models and simulations that careful scaling of synaptic weights can significantly reduce power consumption (by over 80\% in some of the cases tested) while having a relatively small impact on accuracy. These results uncover an exciting opportunity to design neuromorphic systems for edge AI applications, where power consumption can be dynamically adjusted based on energy availability and performance requirements.

翻译：近期动物研究表明，生物大脑在食物匮乏时期可进入低功耗模式。本文探索了在突触权重幅值显著影响功耗的广泛神经形态系统中应用类似机制的可能性。通过数学模型与仿真分析，我们特别证明了：对突触权重进行精细缩放可在准确率影响较小的情况下（部分测试案例中功耗降低超过80%）显著降低系统功耗。这些结果揭示了面向边缘人工智能应用设计神经形态系统的重要机遇——此类系统可根据能源可用性与性能需求动态调节功耗。