Paper Info
Title
An Optical Neural Network Architecture based on Highly Parallelized WDM-Multiplier-Accumulator
Abstract
Future applications such as anomaly detection in a network and autonomous driving require extremely low, submicrosecond latency processing in pattern classification. Towards the realization of such an ultra-fast inference processing, this paper proposes an optical neural network architecture which can classify anomaly patterns at sub-nanosecond latency. The architecture fully exploits optical parallelism of lights using wavelength division multiplexing (WDM) in vector-matrix multiplication. It also exploits a linear optics with passive nanophotonic devices such as microring resonators, optical combiners, and passive couplers, which make it possible to construct low power and ultra-low latency optical neural networks. Optoelectronic circuit simulation using optical circuit implementation of multi-layer perceptron (MLP) demonstrates sub-nanosecond processing of optical neural network.
Year
DOI
Venue
2019
10.1109/PHOTONICS49561.2019.00008
2019 IEEE/ACM Workshop on Photonics-Optics Technology Oriented Networking, Information and Computing Systems (PHOTONICS)
Keywords
Field
DocType
sub-nanosecond latency,linear optics,optical combiners,optical circuit implementation,sub-nanosecond processing,optical neural network architecture,highly parallelized WDM-multiplier-accumulator,anomaly detection,pattern classification,ultra-fast inference processing,anomaly patterns
Wavelength-division multiplexing,Anomaly detection,Latency (engineering),Nanophotonics,Electronic engineering,Optical neural network,Multiplication,Artificial neural network,Optoelectronics,Perceptron,Physics
Conference
ISBN
Citations 
PageRank 
978-1-7281-5982-9
1
0.40
References 
Authors
2
6
Name
Order
Citations
PageRank
Tohru Ishihara171987.96
Shiomi, J.295.37
Naoki Hattori311.07
Yutaka Masuda413.10
Akihiko Shinya595.93
Masaya Notomi699.31