Neuromorphic Silicon Photonics for Artificial Intelligence

2021 ◽  
pp. 417-447
Author(s):  
Bicky A. Marquez ◽  
Chaoran Huang ◽  
Paul R. Prucnal ◽  
Bhavin J. Shastri
Keyword(s):  
Artificial Intelligence ◽  
Silicon Photonics

Accelerating artificial intelligence with silicon photonics

2020 ◽  
Author(s):  
Nicholas C. Harris ◽  
Ryan Braid ◽  
Darius Bunandar ◽  
Jim Carr ◽  
Brad Dobbie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Silicon Photonics

scholarly journals Silicon photonics for artificial intelligence applications

Photoniques ◽  
2020 ◽  
pp. 40-44
Author(s):  
Bicky A. Marquez ◽  
Matthew J. Filipovich ◽  
Emma R. Howard ◽  
Viraj Bangari ◽  
Zhimu Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Efficiency ◽  
High Performance Computing ◽  
Silicon Photonics ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Neuromorphic Engineering ◽  
Software Implementations ◽  
Performance Computing

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.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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