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Machine Learning With Neuromorphic Photonics
Abstract
Neuromorphic photonics has experienced a recent surge of interest over the last few years, promising orders of magnitude improvements in both speed and energy efficiency over digital electronics. This paper provides a tutorial overview of neuromorphic photonic systems and their application to optimization and machine learning problems. We discuss the physical advantages of photonic processing systems, and we describe underlying device models that allow practical systems to be constructed. We also describe several real-world applications for control and deep learning inference. Finally, we 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. The paper is intended for a wide audience and teaches how theory, research, and device concepts from neuromorphic photonics could be applied in practical machine learning systems.
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Photonics for artificial intelligence and neuromorphic computing
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BackgroundAn optical neural chip for implementing complex-valued neural network
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Self-citation
Metadata
- Authors
- de Lima, T. F., Peng, H.-T., Tait, A. N., Nahmias, M. A., Miller, H. B., Shastri, B. J., & Prucnal, P. R.
- Venue
- Journal of Lightwave Technology
- Published
- 2019-03-01
- Volume
- 37
- Issue
- 5
- Pages
- 1515-1534
- ISSN
- 0733-8724 (ISSN-L)
- 1558-2213
- OpenAlex
- W2921972904
- Cited by
- 200
- References
- 14
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- National Science Foundation (1642991)
- Natural Sciences and Engineering Research Council of Canada
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- de Lima, T. F., Peng, H.-T., Tait, A. N., Nahmias, M. A., Miller, H. B., Shastri, B. J., & Prucnal, P. R. (2019). Machine Learning With Neuromorphic Photonics. Journal of Lightwave Technology. https://doi.org/10.1109/jlt.2019.2903474
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