International Association
for Cryptologic Research

Spiking neural networks gain attention due to low power properties and event-based operation, making them suitable for usage in resource constrained embedded devices. Such edge devices allow physical access opening the door for side-channel analysis. In this work, we reverse engineer the parameters of a feed-forward spiking neural network implementation with correlation power analysis. Localized measurements of electro-magnetic emanations enable our attack, despite inherent parallelism and the resulting algorithmic noise of the network. We provide a methodology to extract valuable parameters of integrate-and-fire neurons in all layers, as well as the layer sizes.