Integrated Photonic AI Accelerators under Hardware Security Attacks: Impacts and Countermeasures

TL;DR

This paper examines security vulnerabilities in integrated photonic AI accelerators, analyzing attack impacts on system performance and proposing countermeasures to enhance security in photonic-electronic systems.

Contribution

It identifies novel security threats specific to integrated photonic AI hardware and evaluates potential countermeasures to mitigate these vulnerabilities.

Findings

Photonic system attacks can alter power and phase distributions affecting accuracy.

Security breaches in photonic-electronic interfaces pose unique challenges.

Countermeasures can effectively reduce attack impact on system performance.

Abstract

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.