A framework using measured noise proxies, chance-constrained training, and noise-aware LayerNorm enables Vision Transformers to achieve near-clean accuracy on noisy microring-resonator photonic arrays without in-situ learning or added optical operations.
Resolving the thermal challenges for silicon microring resonator devices.Nanophotonics, 3(4-5):269–281
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Light-Bound Transformers: Hardware-Anchored Robustness for Silicon-Photonic Computer Vision Systems
A framework using measured noise proxies, chance-constrained training, and noise-aware LayerNorm enables Vision Transformers to achieve near-clean accuracy on noisy microring-resonator photonic arrays without in-situ learning or added optical operations.