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.

本文探讨了将生物大脑在食物匮乏时进入低功率模式的类似机制应用于一类神经形态系统中的可能性，其中功耗强烈依赖于突触重量的大小。通过数学模型和仿真，我们特别展示了仔细缩放突触权重可以显著降低功耗（在部分测试用例中达到80％以上），同时对精度的影响相对较小。这些结果揭示了一种令人兴奋的机会来设计边缘AI应用的神经形态系统，其功耗可以根据能源可用性和性能需求进行动态调整。

神经形态系统中突触调整和最优偏置调整以降低功耗