# LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks

arXiv: https://arxiv.org/abs/2204.03835 · HTML: https://mlsanity.com/paper/2204.03835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics](https://mlsanity.com/paper/2401.06885/) (2024)
- [Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks](https://mlsanity.com/paper/2308.03249/) (2023)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)

## Abstract (arXiv)

Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and crosstalk noise, the impact of which accumulates as the network scales up. Leveraging precise device-level models, this paper presents the first comprehensive and systematic optical loss and crosstalk modeling framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380 tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due to optical loss and crosstalk noise.