The Unlikely Hero: Nonideality in Analog Photonic Neural Networks as Built-in Defender Against Adversarial Attacks
Haotian Lu, Ziang Yin, Partho Bhoumik, Sanmitra Banerjee, Krishnendu, Chakrabarty, Jiaqi Gu

TL;DR
This paper reveals that hardware non-idealities in photonic neural networks can be exploited as a built-in defense mechanism against adversarial attacks, offering a novel robustness approach without retraining.
Contribution
It introduces a synergistic defense framework leveraging non-idealities in optical analog hardware to protect against adversarial weight attacks, with efficient optimization and minimal overhead.
Findings
Maintains near-ideal inference accuracy under adversarial attacks
Achieves protection with less than 3% memory overhead
Demonstrates effectiveness across various DNN benchmarks
Abstract
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…
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Taxonomy
TopicsNeural Networks and Reservoir Computing
