| Issue |
Photoniques
Number 104, Septembre-Octobre 2020
|
|
|---|---|---|
| Page(s) | 40 - 44 | |
| Section | Focus: Photonics and Artificial intelligence | |
| DOI | https://doi.org/10.1051/photon/202010440 | |
| Published online | 09 November 2020 | |
Silicon photonics for artificial intelligence applications
1
Department of Physics, Engineering Physics & Astronomy, Queen’s University, Kingston, ON KL7 3N6, Canada
2
Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA
3
Applied Physics Division, National Institute of Standards and Technology, Boulder, CO 80305, USA
* e-mail : bama@queensu.ca
Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.
© The authors, published by EDP Sciences, 2020
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