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Recent animal studies have shown that biological brains can enter a low powermode in times of food scarcity. This paper explores the possibility of applyingsimilar mechanisms to a broad class of neuromorphic systems where powerconsumption is strongly dependent on the magnitude of synaptic weights. Inparticular, we show through mathematical models and simulations that carefulscaling of synaptic weights can significantly reduce power consumption (by over80\% in some of the cases tested) while having a relatively small impact onaccuracy. These results uncover an exciting opportunity to design neuromorphicsystems for edge AI applications, where power consumption can be dynamicallyadjusted based on energy availability and performance requirements.

Synaptic Scaling and Optimal Bias Adjustments for Power Reduction in Neuromorphic Systems