LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Apr 2022.
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Citing papers per year: 2023: 2; 2024: 2.
4 papers.
- 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computingcited by 3
- 2024Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonicscited by 1
- 2023Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networkscited by 0
- 2024Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directionscited by 0
- 2024Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions30 Jul 2024
- 2024Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics12 Jan 2024
- 2023Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks7 Aug 2023
- 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing22 Mar 2023