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"This processor can serve as a universal ultrahigh bandwidth front end for any neuromorphic hardware - optical or electronic based - bringing massive-data machine learning for real-time ultrahigh bandwidth data within reach," says co-lead author of the study, Dr Xu, Swinburne alum and postdoctoral fellow with the Electrical and Computer Systems Engineering Department at Monash University. Micro-combs offer enormous promise for us to meet the world's insatiable need for information," Professor Moss says. "In the 10 years since I co-invented them, integrated micro-comb chips have become enormously important and it is truly exciting to see them enabling these huge advances in information communication and processing. They are much faster, smaller, lighter and cheaper than any other optical source. Micro-combs are relatively new devices that act like a rainbow made up of hundreds of high-quality infrared lasers on a single chip. In contrast, the optical system demonstrated by the team uses a single processor and was achieved using a new technique of simultaneously interleaving the data in time, wavelength and spatial dimensions through an integrated micro-comb source. While state-of-the-art electronic processors such as the Google TPU can operate beyond 100 TeraOPs/s, this is done with tens of thousands of parallel processors. "This breakthrough was achieved with 'optical micro-combs', as was our world-record internet data speed reported in May 2020," says Professor Moss, Director of Swinburne's Optical Sciences Centre and recently named one of Australia's top research leaders in physics and mathematics in the field of optics and photonics by The Australian. 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.
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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. 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 behaviour with unprecedented accuracy and simplicity. Published in the journal Nature, this breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general.Īrtificial 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.