CNN architecture extraction on edge GPU. (arXiv:2401.13575v1 [cs.CR])

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, …

applications architecture art arxiv classification cnn devices edge embedded embedded devices engineering environments etc extraction forecasting gpu image language natural natural language natural language processing network networks neural network neural networks popular recognition resource results reverse reverse engineering speech speech recognition state work