Silicon photonics for artificial intelligence applications

Bicky A. Marquez, Matthew J. Filipovich, Emma R. Howard, Viraj Bangari, Zhimu Guo, Hugh D. Morison, Thomas Ferreira De Lima, Alexander N. Tait, Paul R. Prucnal, Bhavin J. Shastri
2020 Photoniques  
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
more » ... 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.
doi:10.1051/photon/202010440 fatcat:gb2korz4pzc47lpggh76jbr43m