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World's Fastest Optical Neuromorphic Processor | Swinburne University of Technology

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Dr Xingyuan (Mike) Xu with the integrated optical microcomb chip, which forms the core part of the optical neuromorphic processor. Credit: Swinburne University of TechnologyDr Xingyuan (Mike) Xu with the integrated optical microcomb chip, which forms the core part of the optical neuromorphic processor. Credit: Swinburne University of Technology

 

Jan. 7, 2021 (TechXplore) -- An international team of researchers led by Swinburne University of Technology has demonstrated the world's fastest and most powerful optical neuromorphic processor for artificial intelligence (AI), which operates faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large scale data.

 

Published in the prestigious journal Nature, this breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general.

 

Artificial neural networks, a key form of AI, can "learn" and perform complex operations with wide applications to computer vision, natural language processing, facial recognition, speech translation, playing strategy games, medical diagnosis and many other areas. Inspired by the biological structure of the brain's visual cortex system, artificial neural networks extract key features of raw data to predict properties and behavior with unprecedented accuracy and simplicity.

 

Led by Swinburne's Professor David Moss, Dr. Xingyuan (Mike) Xu (Swinburne, Monash University) and Distinguished Professor Arnan Mitchell from RMIT University, the team achieved an exceptional feat in optical neural networks: dramatically accelerating their computing speed and processing power.

 

The team demonstrated an optical neuromorphic processor operating more than 1000 times faster than any previous processor, with the system also processing record-sized ultra-large scale images -- enough to achieve full facial image recognition, something that other optical processors have been unable to accomplish.

 

(more)

 

READ MORE: TechXplore

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  • Created
    Thursday, January 07 2021
  • Last modified
    Monday, January 11 2021
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