# Design of optical neural networks with component imprecisions

arXiv: https://arxiv.org/abs/2001.01681 · HTML: https://mlsanity.com/paper/2001.01681/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation](https://mlsanity.com/paper/2303.03986/) (2023)
- [Characterizing Coherent Integrated Photonic Neural Networks under Imperfections](https://mlsanity.com/paper/2207.10835/) (2022)
- [Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2112.07485/) (2021)
- [Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers](https://mlsanity.com/paper/2210.09171/) (2022)
- [Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs](https://mlsanity.com/paper/2111.14787/) (2021)
- [DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators](https://mlsanity.com/paper/2403.02688/) (2024)
- [Acceleration Method for Learning Fine-Layered Optical Neural Networks](https://mlsanity.com/paper/2109.01731/) (2021)
- [Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets](https://mlsanity.com/paper/2210.08004/) (2022)
- [Thermal Crosstalk Modelling and Compensation Methods for Programmable Photonic Integrated Circuits](https://mlsanity.com/paper/2404.10589/) (2024)
- [The Impact of Feature Representation on the Accuracy of Photonic Neural Networks](https://mlsanity.com/paper/2406.18757/) (2024)

## Abstract (arXiv)

For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs -- one with a more tunable design (GridNet) and one with better fault tolerance (FFTNet) -- to classify handwritten digits. When simulated without any imperfections, GridNet yields a better accuracy (~98%) than FFTNet (~95%). However, under a small amount of error in their photonic components, the more fault tolerant FFTNet overtakes GridNet. We further provide thorough quantitative and qualitative analyses of ONNs' sensitivity to varying levels and types of imprecisions. Our results offer guidelines for the principled design of fault-tolerant ONNs as well as a foundation for further research.