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28 September 2023 Analog photonic neural networks for large-scale AI at the quantum limit
Logan G. Wright, Peter L. McMahon, Tatsuhiro Onodera, Tianyu Wang
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265510 (2023) https://doi.org/10.1117/12.2680563
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
I will overview our work on analog neural networks based on photonics and other controllable physical systems. I will show how backpropagation can efficiently train physical neural networks (PNNs), and how to design physical network architectures for physics-based machine learning. I will review our work showing how nonlinear photonic neural networks may enhance computational sensing and how photonic neural networks may be operated robustly deep into low-energy regimes where quantum noise would ordinarily be a limiting factor. Finally, I will show that PNNs offer fundamental advantages for scaling AI models such as Transformers.
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Logan G. Wright, Peter L. McMahon, Tatsuhiro Onodera, and Tianyu Wang "Analog photonic neural networks for large-scale AI at the quantum limit", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265510 (28 September 2023); https://doi.org/10.1117/12.2680563
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KEYWORDS
Neural networks
Analog electronics
Artificial intelligence
Quantum limits
Education and training
Computing systems
Photons
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