AI Boosted by Parallel Convolutional Light-Based Processors
Schematic representation of a processor for matrix multiplications which runs on light.
Credit: University of Oxford
The exponential growth of data traffic in our digital age poses some real challenges on processing power. And with the advent of machine learning and 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.
Now, an international team of scientists has turned to light to tackle the problem. The researchers 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 the study’s leads. Professor Kippenberg’s research has pioneered the development of frequency combs. “The frequency comb provides a variety of optical wavelengths that are processed independently of one another in the same photonic chip.”
“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 Münster University, one of the professors who led the research. “This is much faster than conventional chips which rely on electronic data transfer, such as graphic cards or specialized hardware like TPU’s (Tensor Processing Unit).”