Light-carrying chips advance machine learning, say experts

The researchers from the Swiss Federal Institute of Technology have developed a new light-based approach to combine processing and data storage onto a single chip.

The researchers from the Swiss Federal Institute of Technology have developed a new light-based approach to combine processing and data storage onto a single chip. The exponential growth of data traffic in our digital age poses some real challenges in processing power. And with the advent of machine learning and Artificial Intelligence (AI) in, for example, self-driving vehicles and speech recognition, the upward trend is set to continue. All this places a heavy burden on the ability of current computer processors to keep up with demand.

To tackle the problem, the researchers have developed a new approach and architecture that combines processing and data storage onto a single chip by using light-based, or 'photonic' processors, which are shown to surpass conventional electronic chips by processing information much more rapidly and in parallel. The scientists developed a hardware accelerator for so-called matrix-vector multiplications, which are the backbone of neural networks (algorithms that simulate the human brain), which themselves are used for machine-learning algorithms.

Since different light wavelengths (colors) don't interfere with each other, the researchers could use multiple wavelengths of light for parallel calculations. But to do this, they used another innovative technology, developed at EPFL, a chip-based "frequency comb", as a light source. "Our study is the first to apply frequency combs in the field of artificial neural networks," says Professor Tobias Kippenberg at EPFL, one of the study's leads.

"Light-based processors for speeding up tasks in the field of machine learning enable complex mathematical tasks to be processed at high speeds and throughputs," says senior co-author Wolfram Pernice at Munster University, one of the professors who led the research. After designing and fabricating the photonic chips, the researchers tested them on a neural network that recognizes hand-written numbers. Inspired by biology, these networks are a concept in the field of machine learning and are used primarily in the processing of image or audio data.

"This work is a real showcase of European collaborative research," says David Wright at the University of Exeter, who leads the EU project FunComp, which funded the work. The study is published in Nature and has far-reaching applications: higher simultaneous (and energy-saving) processing of data in AI, larger neural networks for more accurate forecasts and more precise data analysis, large amounts of clinical data for diagnoses, enhancing rapid evaluation of sensor data in self-driving vehicles, and expanding cloud computing infrastructures with more storage space, computing power, and applications software. 

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