CNN architecture extraction on edge GPU
Peter Horvath Lukasz Chmielewski Leo Weissbart Lejla Batina Yuval Yarom
Neural networks have become popular due to their versatility and
state-of-the-art results in many applications, such as image classification,
natural language processing, speech recognition, forecasting, etc. These
applications are also used in resource-constrained environments such as
embedded devices. In this work, the susceptibility of neural network
implementations to reverse engineering is explored on the NVIDIA Jetson Nano
microcomputer via side-channel analysis. To this end, an architecture
extraction attack is presented. In the attack, 15 popular convolutional neural
network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented
on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is
analyzed during the inference operation of the neural networks. The results of
the analysis show that neural network architectures are easily distinguishable
using deep learning-based side-channel analysis.