Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based
  Inference Accelerators
Hansika Weerasena Prabhat Mishra
Convolution Neural Networks (CNNs) are widely used in various domains. Recent
advances in dataflow-based CNN accelerators have enabled CNN inference in
resource-constrained edge devices. These dataflow accelerators utilize inherent
data reuse of convolution layers to process CNN models efficiently. Concealing
the architecture of CNN models is critical for privacy and security. This paper
evaluates memory-based side-channel information to recover CNN architectures
from dataflow-based CNN inference accelerators. The proposed attack exploits
spatial and temporal data reuse of the dataflow mapping on CNN accelerators and
architectural hints to recover the structure of CNN models. Experimental
results demonstrate that our proposed side-channel attack can recover the
structures of popular CNN models, namely Lenet, Alexnet, and VGGnet16.