Serve as a universal ultrahigh bandwidth front end for neuromorphic hardware

 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,” said Xingyuan (Mike) Xu, a research fellow at Monash University who co-led the research. “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 micro combs.”

The research was published in Nature under the title “11 TOPS photonic convolutional accelerator for optical neural networks.” The article was written by Xingyuan Xu, Mengxi Tan, Bill Corcoran, Jiayang Wu, Andreas Boes, Thach G. Nguyen, Sai T. Chu, Brent E. Little, Damien G. Hicks, Roberto Morandotti, computer engineering vs computer science.

Neuromorphic computing, which uses chips that mimic the behavior of the human brain using virtual “neurons,” is growing in popularity thanks to high-profile efforts from Intel and others. Now, a team of researchers led by the Swinburne University of Technology in Melbourne is announcing that it has demonstrated what it’s calling “the world’s fastest and most powerful optical neuromorphic processor for artificial intelligence.”