Error-Tolerant Integrated Optical Neural Network Processor based on Multi-Plane Light Conversion

Tanomura, Ryota; Mizukami, Keigo; Tang, Rui; Soma, Go; Tanemura, Takuo; Nakano, Yoshiaki

doi:10.1364/cleo_at.2022.jth3a.49

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…By addressing the mismatch between simulated training and experimental setup, the full potential of optical neural networks can be realized. [76][77][78][79][80][81][82] Due to the lack of monitoring for intermediate status, implementing gradient descent algorithms 83 for training optical neural networks on real optical systems can prove to be difficult. [84][85][86][87][88][89][90][91][92][93][94][95] This presents a challenge for achieving optimal performance in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Training on the optical system: local search method

Nouri

Dalir

et al. 2023

AI and Optical Data Sciences IV

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Optical neural networks hold tremendous potential for energy efficiency and low latency. Despite this potential, the mismatch between simulation training and experimental setups can negatively impact the system's performance. To address this issue, we present a local search method for training optical neural networks in the system. The implementation of this training method allows optical neural networks to reach their state-of-the-art performance levels within the constraints of current experimental settings.

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Section: Introductionmentioning

confidence: 99%

Training on the optical system: local search method

Nouri

Dalir

et al. 2023

AI and Optical Data Sciences IV

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“…There are various methods to address the mismatch degradation in optical systems, [76][77][78][79][80][81][82] such as using misalignment tolerance in neural network parameters or fine-tuning the parameters from real systems. Previous studies attempted to train the system through simulations with the aim of building an optical system that perfectly matched the design, but this resulted in a 20% reduction in classification accuracy compared to the simulation.…”

Section: Introductionmentioning

confidence: 99%

Black-box simulation method: train the optical model from output

Hu¹,

Nouri²,

Dalir³

et al. 2023

Physics and Simulation of Optoelectronic Devices XXXI

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Accurate simulation is a critical requirement for modern optical systems, but precise theoretical modeling can be challenging due to various factors such as misalignment, theoretical approximations, and instrument errors. This paper presents the black-box simulation method, which addresses these limitations by training the optical system model using the optical field outputs. This approach leads to more accurate simulations, making it possible to effectively train optical Neural Network systems for improved performance.

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