Photonics for artificial intelligence and neuromorphic computing
By Bhavin J. Shastri, Alexander N. Tait, T. F. D. Lima, Wolfram H. P. Pernice, Harish Bhaskaran, C. D. Wright, Paul R. Prucnal. Nature Photonics, 2020
1,655 citations recorded by the source corpus.
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/5e7a7fb69ef7447c2cb8966589e49a5acbee416e
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- Lukas Chrostowski
- Peter Leonard McMahon
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- Sudip Shekhar
- Bhavin J. Shastri
- Wim Bogaerts
- Shankar Kumar Selvaraja
- R. Baets
Citations in the corpus
- The physics of optical computing (2023, methodology): “…in which case the gain is essentially provided electronically by transistors, a common architectural choice in optical-neural-network prototypes 2 that often also serves the dual purpose of providing nonlinearity), and its required speed, preservation of information encoded in the optical…”
- Roadmapping the next generation of silicon photonics (2023, background): “Additionally, for neural networks, efficiently implementing programmable nonlinearities stands out as a significant hurdle [37].”