The processor is able to operate at faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large scale data.
Details have been published in Nature, and the breakthrough is seen as representing an enormous leap forward for neural networks and neuromorphic processing in general.
Artificial neural networks can 'learn' and perform complex operations and are 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 high levels of 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 were able to dramatically accelerate 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'," said 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.
"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," added 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," explained co-lead author of the study, Dr Xu.
While convolutional neural networks have been central to the artificial intelligence revolution, existing silicon technology is seen as a growing bottleneck in processing speed and energy efficiency.
"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 explained.