Title: Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators Show Chinese title

Title: 通过数据流推理加速器中的侧信道分析揭示卷积神经网络架构

Abstract: 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. Show Chinese abstract

Abstract: 卷积神经网络（CNNs）在各个领域被广泛使用。 基于数据流的CNN加速器的最新进展使得在资源受限的边缘设备上进行CNN推理成为可能。 这些数据流加速器利用卷积层的固有数据重用来高效处理CNN模型。 隐藏CNN模型的架构对于隐私和安全至关重要。 本文评估了基于内存的侧信道信息，以从基于数据流的CNN推理加速器中恢复CNN架构。 所提出的攻击利用了数据流映射在CNN加速器上的空间和时间数据重用以及架构提示来恢复CNN模型的结构。 实验结果表明，我们提出的侧信道攻击可以恢复流行CNN模型的结构，即Lenet、Alexnet和VGGnet16。