On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks

• URL: http://arxiv.org/abs/2312.07594v1
• Date: Mon, 11 Dec 2023 10:13:53 GMT
• Title: On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks
• Authors: Amalia Artemis Koufopoulou, Athanasios Papadimitriou, Aggelos Pikrakis, Mihalis Psarakis, David Hely,
• Abstract summary: We propose an evaluation methodology of hardware security properties of HLS-produced designs using state-of-the-art Graph Neural Network (GNN) approaches. We show that GNNs can be efficiently trained to predict important hardware security met-rics concerning fault attacks. The proposed method predicts the fault vulnerability metrics of the HLS-based designs with high R-squared scores and achieves huge speedup.
• Score: 1.6951945839990796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS design flow. Yet the need to evaluate a huge number of functionally equivalent de-signs of the HLS design space challenges hardware security evaluation methods (e.g., fault injection - FI campaigns). In this work, we propose an evaluation methodology of hardware security properties of HLS-produced designs using state-of-the-art Graph Neural Network (GNN) approaches that achieves significant speedup and better scalability than typical evaluation methods (such as FI). We demonstrate the proposed methodology on a Double Modular Redundancy (DMR) coun-termeasure applied on an AES SBox implementation, en-hanced by diversifying the redundant modules through HLS directives. The experimental results show that GNNs can be efficiently trained to predict important hardware security met-rics concerning fault attacks (e.g., critical and detection error rates), by using regression. The proposed method predicts the fault vulnerability metrics of the HLS-based designs with high R-squared scores and achieves huge speedup compared to fault injection once the training of the GNN is completed.

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