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arXiv · 2510.14120

Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing

Muhammad Faheemur Rahman·Wayne Burleson
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Abstract

Memristive crossbar arrays (MCA) are emerging as efficient building blocks for in-memory computing and neuromorphic hardware due to their high density and parallel analog matrix-vector multiplication capabilities. However, the physical properties of their nonvolatile memory elements introduce new attack surfaces, particularly under fault injection scenarios. This work explores Laser Fault Injection as a means of inducing analog perturbations in MCA-based architectures. We present a detailed threat model in which adversaries target memristive cells to subtly alter their physical properties or outputs using laser beams. Through HSPICE simulations of a large MCA on 45 nm CMOS tech. node, we show how laser-induced photocurrent manifests in output current distributions, enabling differential fault analysis to infer internal weights with up to 99.7% accuracy, replicate the model, and compromise computational integrity through targeted weight alterations by approximately 143%.

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Muhammad Faheemur Rahman, Wayne Burleson. 2025-10-15. Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing. https://arxiv.org/abs/2510.14120

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