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

arXiv: https://arxiv.org/abs/2306.07416 · HTML: https://mlsanity.com/paper/2306.07416/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Adaptive Neural Networks for Efficient Inference](https://mlsanity.com/paper/1702.07811/) (2017)
- [Dynamic Power Management for Neuromorphic Many-Core Systems](https://mlsanity.com/paper/1903.08941/) (2019)
- [Exploring Energy-Accuracy Tradeoffs in AI Hardware](https://mlsanity.com/paper/2011.08779/) (2020)

## Abstract (arXiv)

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.