aiqbench analysis
This paper investigates hardware Trojan threats specifically targeting multi-chiplet photonic neural network accelerators, addressing the unique security vulnerabilities introduced by heterogeneous chiplet integration and distributed architectures. It analyzes risks to confidentiality, integrity, and availability in a domain where AI acceleration relies on specialized photonic computing rather than standard electronic chips. The work is critical for understanding the physical layer security challenges inherent in next-generation, energy-efficient AI hardware designs.
Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators
arXiv:2609.06796v1 Announce Type: cross Abstract: Multi-chiplet photonic neural network accelerators (MCPNAs) combine photonic computation, photonic communica-tion, and heterogeneous chiplet integration to enable scalable and energy-efficient AI acceleration. However, their distributed archi-tecture and reliance on third-party chiplets introduce significant hardware security risks. This paper examines Hardware Trojan (HT) threats to MCPNAs across three dimensions: confidentiality, integrity, and availability.
Abstract:Multi-chiplet photonic neural network accelerators (MCPNAs) combine photonic computation, photonic communica-tion, and heterogeneous chiplet integration to enable scalable and energy-efficient AI acceleration. However, their distributed archi-tecture and reliance on third-party chiplets introduce significant hardware security risks. This paper examines Hardware Trojan (HT) threats to MCPNAs across three dimensions: confidentiality, integrity, and availability.
Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.06796 [cs.AR]
(or arXiv:2609.06796v1 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2609.06796 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 152 words
