A Threshold Implementation-Based Neural Network Accelerator With Power And Electromagnetic Side-Channel Countermeasures

With the recent advancements in machine learning (ML) theory, a lot of energy-efficient neural network (NN) accelerators have been developed. However, their associated side-channel security vulnerabilities pose a major concern. There have been several proof-of-concept attacks demonstrating the extraction of their model parameters and input data. This work introduces a threshold implementation (TI) masking-based NN accelerator that secures model parameters and inputs against power and electromagnetic (EM) side-channel attacks. The 0.159 mm 2 demonstration in 28 nm runs at 125 MHz at 0.95 V and limits the area and energy overhead to 64% and 5.5 × , respectively, while demonstrating security even greater than 2M traces. The accelerator also secures model parameters through encryption and the inputs against horizontal power analysis (HPA) attacks.