Silicon Photonics-based AI Accelerators (SPAAs) have been considered as promising AI accelerators achieving high energy efficiency and low latency. While many researchers focus on improving SPAAs' energy efficiency and latency, their physical security has not been sufficiently studied. This paper first proposes a threat of thermal fault injection attacks on SPAAs based on Vector-Matrix Multipliers (VMMs) utilizing Mach-Zhender Interferometers. This paper then proposes SecONN, an optical neural network framework that is capable of not only inferences but also concurrent detection of the attacks. In addition, this paper introduces a concept of Wavelength Division Perturbation (WDP) where wavelength dependent VMM results are utilized to increase detection accuracy. Simulation results show that the proposed method achieves 88.7% attack-caused average misprediction recall.

翻译：基于硅光子的AI加速器（SPAAs）因其高能效与低延迟特性，被视为极具前景的AI加速器。当前多数研究聚焦于提升SPAAs的能效与延迟性能，但其物理安全性尚未得到充分探究。本文首次提出针对基于马赫-曾德尔干涉仪的向量-矩阵乘法器（VMMs）型SPAAs的热故障注入攻击威胁。随后提出SecONN光学神经网络框架，该框架不仅具备推理能力，还可实现对攻击的并发检测。此外，本文引入波长扰动（WDP）概念，通过利用波长相关的VMM计算结果提升检测精度。仿真结果表明，所提方法对攻击导致的平均误判召回率达到88.7%。