Device-Informed Training Strategy for Low-Precision Photonic Neural Networks
This paper compares three training strategies for low-precision photonic neural networks. The efficacy of the schemes is tested through circuital simulations on the photonic electronic multiplyaccumulate neuron (PEMAN) architecture with three different datasets.
Authors
- Sumanta Gupta (ORCID: https://orcid.org/0000-0001-8483-9572)
- Nicola Andriolli (ORCID: https://orcid.org/0000-0001-8322-6854)
- G. Contestabile (ORCID: https://orcid.org/0000-0001-8703-3291)
- Ankush Anand (ORCID: https://orcid.org/0000-0002-3056-9594)
- Lorenzo De Marinis (ORCID: https://orcid.org/0000-0003-1462-3199)
- Emilio Paolini (ORCID: https://orcid.org/0000-0001-7486-9268)
- Jimson Mathew (ORCID: https://orcid.org/0000-0001-8247-9040)
- D. Sahoo (ORCID: https://orcid.org/0000-0002-5600-9413)
- Vikas Kumar Keshari
Institutions
- University of Pisa (IT)
- Indian Institute of Technology Patna (IN)
- Scuola Superiore Sant'Anna (IT)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11110766
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00