Dr Xingyuan (Mike) Xu with the integrated optical microcomb chip, which forms the core part of the optical neuromorphic processor.
- 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
- This breakthrough has been published in the prestigious journal Nature and represents an enormous leap forward for neural networks and neuromorphic processing
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 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.
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,” says Professor Moss.
“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.”
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Optical neuromorphic processor
- Materials scientists reveal pathway for designing optical materials with specialized properties
While we usually think of disorder as a bad thing, a team of materials science researchers led by Rohan Mishra, from Washington University in St. Louis, and Jayakanth Ravichandran, from the University ...
- Production begins on first optical beamforming chip
SkyWater Technology and Lumotive have started production on what they claim is the world’s first programmable optical metasurface that has been commercialized into a mass-producible chip.
- SkyWater and Lumotive start production of optical beamforming chip
SkyWater Technology and Lumotive, a developer of optical semiconductor technology for 3D sensing, have started production implementation of Lumotive’s solid-state optical beamforming technology in ...
- Research Bits: April 30
The team sees potential for use in a new class of optical neuromorphic computing which could be reconfigured spontaneously and would allow large-scale in-memory computing in the present ...
- AI Efficiency Breakthrough: How Sound Waves Are Revolutionizing Optical Neural Networks
Researchers have developed a way to use sound waves in optical neural networks, enhancing their ability to process data with high speed and energy efficiency. Optical neural networks may provide the ...
Go deeper with Google Headlines on:
Optical neuromorphic processor
[google_news title=”” keyword=”optical neuromorphic processor” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]
Go deeper with Bing News on:
Neuromorphic processor
- BrainChip and Frontgrade Gaisler to Augment Space-Grade Microprocessors with AI Capabilities
BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, and Frontgrade Gaisler, a leading ...
- Sandia Pushes The Neuromorphic AI Envelope With Hala Point “Supercomputer”
Not many devices in the datacenter have been etched with the Intel 4 process, which is the chip maker’s spin on 7 nanometer extreme ultraviolet immersion ...
- ‘Neuromorphic’ chip modeled after the human brain aims to bring smarts to computers
Is the outsourcing of IT jobs a form of discrimination? A group of laid-off IT workers at the University of California, San Francisco may raise this very question as part of a possible lawsuit ...
- Intel's Hala Point, the world's largest neuromorphic computer, has 1.15 billion neurons
The Hala Point system's 1,152 Loihi 2 chips enable a total of 1.15 billion artificial neurons, Intel said, "and 128 billion synapses distributed over 140,544 neuromorphic processing cores." That is an ...
- Intel builds the world's largest neuromorphic system 'Hala Point' with a processing capacity of 20 quadrillion times per second
This Hala Point is equipped with the neuromorphic chip 'Loihi 2' announced by Intel in 2021. The Loihi2 chip is manufactured using the Intel 4 process, has an area of only 31 mm2 per chip ...
Go deeper with Google Headlines on:
Neuromorphic processor
[google_news title=”” keyword=”neuromorphic processor” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]