The Unlikely Hero: Nonideality in Analog Photonic Neural Networks as Built-in Defender Against Adversarial Attacks
<|reference_start|>The Unlikely Hero: Nonideality in Analog Photonic Neural Networks as Built-in Defender Against Adversarial Attacks: Electronic-photonic computing systems have emerged as a promising platform for accelerating deep neural network (DNN) workloads. Major efforts have been focused on countering hardware non-idealities and boosting efficiency with various hardware/algorithm co-design methods. However, the adversarial robustness of such photonic analog mixed-signal AI hardware remains unexplored. Though the hardware variations can be mitigated with robustness-driven optimization methods, malicious attacks on the hardware show distinct behaviors from noises, which requires a customized protection method tailored to optical analog hardware. In this work, we rethink the role of conventionally undesired non-idealities in photonic analog accelerators and claim their surprising effects on defending against adversarial weight attacks. Inspired by the protection effects from DNN quantization and pruning, we propose a synergistic defense framework tailored for optical analog hardware that proactively protects sensitive weights via pre-attack unary weight encoding and post-attack vulnerability-aware weight locking. Efficiency-reliability trade-offs are formulated as constrained optimization problems and efficiently solved offline without model re-training costs. Extensive evaluation of various DNN benchmarks with a multi-core photonic accelerator shows that our framework maintains near-ideal on-chip inference accuracy under adversarial bit-flip attacks with merely <3% memory overhead. Our codes are open-sourced at https://github.com/ScopeX-ASU/Unlikely_Hero.<|reference_end|>
@article{lu2024the,
    title={The Unlikely Hero: Nonideality in Analog Photonic Neural Networks as
  Built-in Defender Against Adversarial Attacks},
    author={Haotian Lu, Ziang Yin, Partho Bhoumik, Sanmitra Banerjee, Krishnendu
  Chakrabarty, Jiaqi Gu},
    journal={arXiv preprint arXiv:2410.01289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01289},
    primaryClass={cs.ET cs.CR physics.optics}
}