Integrated photonics based on silicon photonics platform is driving several application domains, from enabling ultra-fast chip-scale communication in high-performance computing systems to energy-efficient optical computation in artificial intelligence (AI) hardware accelerators. Integrating silicon photonics into a system necessitates the adoption of interfaces between the photonic and the electronic subsystems, which are required for buffering data and optical-to-electrical and electrical-to-optical conversions. Consequently, this can lead to new and inevitable security breaches that cannot be fully addressed using hardware security solutions proposed for purely electronic systems. This paper explores different types of attacks profiting from such breaches in integrated photonic neural network accelerators. We show the impact of these attacks on the system performance (i.e., power and phase distributions, which impact accuracy) and possible solutions to counter such attacks.

翻译：基于硅光平台的集成光子学正推动多个应用领域的发展，从实现高性能计算系统中超快芯片级通信，到人工智能硬件加速器中的节能光计算。将硅光集成到系统中需要采用光子子系统与电子子系统之间的接口，这些接口用于数据缓冲、光电转换和电光转换。因此，这可能导致新的不可避免的安全漏洞，而这些漏洞无法通过专为纯电子系统提出的硬件安全解决方案完全解决。本文探讨了利用集成光子神经网络加速器中此类漏洞的不同攻击类型。我们展示了这些攻击对系统性能（即影响精度的功率和相位分布）的影响，以及应对此类攻击的可行解决方案。