On the Impact of Uncertainties in Silicon-Photonic Neural Networks
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
2. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Link
http://xplorestaging.ieee.org/ielx7/6221038/10066123/09766130.pdf?arnumber=9766130
Reference12 articles.
1. Efficient, compact and low loss thermo-optic phase shifter in silicon
2. Low-loss, high-bandwidth fiber-to-chip coupling using capped adiabatic tapered fibers
3. Optimizing Coherent Integrated Photonic Neural Networks under Random Uncertainties
4. Modeling Silicon-Photonic Neural Networks under Uncertainties
5. Nanophotonic integration in state-of-the-art CMOS foundries
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1. Integrated Photonic AI Accelerators Under Hardware Security Attacks: Impacts and Countermeasures;2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS);2023-08-06
2. Transfer-Matrix Abstractions to Analyze the Effect of Manufacturing Variations in Silicon Photonic Circuits;2022 IEEE International Test Conference India (ITC India);2022-07-24
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