Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909849656229888 |
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| author | Rahman, Muhammad Faheemur Burleson, Wayne |
| author_facet | Rahman, Muhammad Faheemur Burleson, Wayne |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing Rahman, Muhammad Faheemur Burleson, Wayne Emerging Technologies Neural and Evolutionary Computing Systems and Control 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%. |
| title | Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing |
| topic | Emerging Technologies Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2510.14120 |