Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators

Sudeep Pasricha

cs.ARSep 6, 2026

Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators

Authors: Sudeep Pasricha

Organizations: Department of Electrical and Computer Engineering Colorado State University

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.

Cybersecurity

Cybersecurity develops methods to protect digital and cyber-physical systems from attacks, vulnerabilities, data loss, and misuse.

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Jul 26, 2026J/K move · Enter open · S save

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA

Cybersecurity

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Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale. The proposed approach incorporates symbolically enabled learning by modeling flattened gate-level netlists as Boolean networks represented as And-Inverter Graphs (AIGs), where all internal nodes are 2-input AND gates and inversions reside on the edges. Each directed connection is expressed as a triple within a Knowledge Graph Embedding (KGE) framework, producing compact, constant-size per-node representations that retain multi-hop structural context. The AIG's bounded fan-in and uniform semantics ensure training and inference complexity scale linearly with edge count, addressing major scalability bottlenecks in HT detection. Symbolically enabled learning across deep datapaths enables the model to differentiate circuit structures from rare and functionally inconsistent connections that signify potential Trojan triggers and payloads. Experiments on large-scale SoC benchmarks demonstrate clear geometric separation between Trojan and benign nodes and practical scalability.

Abstract

Cybersecurity

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