AutoSkewBMT: Autonomously Synthesizing Optimized Integrity Authentication Mechanism for DNN Accelerators

As domain-specific accelerators for deep neural network (DNN) inference gain popularity due to their performance and flexibility advantages over general-purpose systems, the security of accelerator data in memory has emerged as a significant concern. However, the overhead associated with standard memory security measures, such as encryption and integrity authentication, presents a major challenge for accelerators, particularly given the high throughput demands of typical DNN applications [1]. In this work, we present AutoSkewBMT, a security framework that autonomously generates optimized integrity system configurations to enhance the Bonsai Merkle Tree (BMT)-based integrity authentication workflow for DNN accelerators. The framework leverages a novel and efficient design space generation algorithm to optimally skew the BMT for specific workloads. Configurations generated by AutoSkewBMT outperform recent state-of-the-art solutions by up to 32% on general DNN workloads.