Power Analysis Attacks on NVM Crossbar-based Neuromorphic Systems

This paper proposes a new adversarial attack strategy against neuromorphic systems using analysis of power consumption. Specifically, we show that neuromorphic designs based on non-volatile memory crossbars can leak important information about loss sensitivity in their power profile. Adversaries can use this information to craft evasion attacks even if they don’t know the dataset that the model was trained on. In our experiments, we show that these types of attacks are effective against both single-layer and multilayer neuromorphic implementations of neural networks, and they can be made query-efficient through Bayesian optimization. We also provide theoretical insights into the relationship between the loss sensitivity and the power consumption measurements, showing that, for single-layer networks, the correlation coefficient of these two metrics scales inversely with the square root of the input size. Finally, this paper proposes that low bitwidth quantization could be an effective defense strategy against the class of attacks discussed herein.