World's fastest optical neuromorphic processor

Date:
January 7, 2021
Source:
Monash University
Summary:
A Swinburne-led team has demonstrated the world's fastest and
most powerful optical neuromorphic processor for artificial
intelligence. The neuromorphic processor operates faster than
10 trillion operations per second and is capable of processing
ultra-large scale data.



FULL STORY ==========================================================================
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 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 behaviour 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.

"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.



========================================================================== 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. 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.

Micro-combs are relatively new devices that act like a rainbow made up
of hundreds of high-quality infrared lasers on a single chip. They are
much faster, smaller, lighter and cheaper than any other optical source.

"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. Micro-combs offer enormous promise for us to meet the world's insatiable need for information," Professor Moss says.

"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.

"We're currently getting a sneak-peak of how the processors of the
future will look. It's really showing us how dramatically we can scale
the power of our processors through the innovative use of microcombs,"
Dr Xu explains.

RMIT's Professor Mitchell adds, "This technology is applicable to all
forms of processing and communications -- it will have a huge impact. Long
term we hope to realise fully integrated systems on a chip, greatly
reducing cost and energy consumption." "Convolutional neural networks
have been central to the artificial intelligence revolution, but existing silicon technology increasingly presents a bottleneck in processing speed
and energy efficiency," says key supporter of the research team, Professor Damien Hicks, from Swinburne and the Walter and Elizabeth Hall Institute.

He adds, "This breakthrough shows how a new optical technology makes such networks faster and more efficient and is a profound demonstration of
the benefits of cross-disciplinary thinking, in having the inspiration
and courage to take an idea from one field and using it to solve a
fundamental problem in another."

========================================================================== Story Source: Materials provided by Monash_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Xingyuan Xu, Mengxi Tan, Bill Corcoran, Jiayang Wu, Andreas Boes,
Thach
G. Nguyen, Sai T. Chu, Brent E. Little, Damien G. Hicks, Roberto
Morandotti, Arnan Mitchell, David J. Moss. 11 TOPS photonic
convolutional accelerator for optical neural networks. Nature,
2021; 589 (7840): 44 DOI: 10.1038/s41586-020-03063-0 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/01/210107112418.htm

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