Deep Learning-Based Power Side-Channel Evaluation from RTL Switching-Activity Proxies

Deep learning has become a central tool in power side-channel analysis, supporting not only key recovery from measured traces but also the detection of key-dependent patterns, templates, and leakage structure. Despite this progress, the use of deep learning for leakage assessment at the Register-Transfer Level (RTL) remains largely unexamined. RTL-level switching activity provides the earliest point in the design flow where power side-channel leakage can be assessed, offering fast analysis while avoiding the complexity and cost associated with gate-level evaluation. This paper presents the first study that applies both unsupervised and supervised deep learning to power side-channel evaluation at RTL-level. An unsupervised Convolutional Long Short-Term Memory (ConvLSTM) autoencoder is used to learn temporal representations without key labels, and a supervised Convolutional Neural Network (CNN) is included to provide a profiling-style reference. Both models are evaluated using 40,000 RTL switching-activity proxies generated with VeriSide for AES 128 executions on a CVA6 core with an AES-64 accelerator. The unsupervised model achieves precise key distinguishability and consistent leakage localization, approaching the supervised baseline with moderately higher trace requirements. The results show that deep learning can expose exploitable leakage directly at the RTL stage, motivating the integration of mitigation strategies early in the digital design process.