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AutoSkewBMT: Autonomously Synthesizing Optimized Integrity Authentication Mechanism for DNN Accelerators
Conference paper

AutoSkewBMT: Autonomously Synthesizing Optimized Integrity Authentication Mechanism for DNN Accelerators

Rakin Muhammad Shadab, Sanjay Gandham and Mingjie Lin
Proceedings of the 62nd Annual ACM/IEEE Design Automation Conference, pp.1-7
ACM Conferences, IEEE Press
DAC '25: 62nd Annual ACM/IEEE Design Automation Conference (San Francisco, CA, 06/22/2025–06/25/2025)
06/22/2023
DOI: https://doi.org/10.1109/DAC63849.2025.11132968
Appears in  Artificial Intelligence and Machine Learning Research

Abstract

Computer systems organization Computer systems organization -- Architectures Computer systems organization -- Architectures -- Other architectures Computer systems organization -- Architectures -- Other architectures -- Neural networks Computing methodologies Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Machine learning approaches Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks Hardware Hardware -- Integrated circuits Hardware -- Integrated circuits -- Reconfigurable logic and FPGAs Hardware -- Integrated circuits -- Reconfigurable logic and FPGAs -- Hardware accelerators Security and privacy Security and privacy -- Security in hardware Security and privacy -- Security in hardware -- Hardware security implementation Security and privacy -- Systems security Security and privacy -- Systems security -- Operating systems security Security and privacy -- Systems security -- Operating systems security -- Trusted computing
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.
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