Silicon Photonic Neural Networks and Applications
- Resource Type
- Conference
- Authors
- Shastri, B. J.; Marquez, B. A.; Tait, A. N.; Ferreira de Lima, T.; Peng, H. -T.; Huang, C.; Prucnal, P. R.
- Source
- 2020 Photonics North (PN) Photonics North (PN), 2020. :1-1 May, 2020
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Photonics and Electrooptics
Robotics and Control Systems
Transportation
Photonics
Machine learning
Neuromorphics
Silicon
Computational modeling
Neural networks
Computer architecture
Silicon photonics
neuromorphic computing
machine learning
optical neural networks
optical computing
- Language
Neuromorphic photonic processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. We will provide an overview of neuromorphic photonic systems and their application to machine learning and specifically deep learning inference with a hybrid digital electronics and analog photonics architecture based on silicon photonics. We will discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms.