MIT researchers have developed a novel "photonic" chip that uses light instead of electricity—and consumes relatively little power in the process. The chip could be used to process massive neural networks millions of times more efficiently than today's classical computers do.
Neural networks are machine-learning models that are widely used for such tasks as robotic object identification, natural language processing, drug development, medical imaging, and powering driverless cars. Novel optical neural networks, which use optical phenomena to accelerate computation, can run much faster and more efficiently than their electrical counterparts.
But as traditional and optical neural networks grow more complex, they eat up tons of power. To tackle that issue, researchers and major tech companies—including Google, IBM, and Tesla—have developed "AI accelerators," specialized chips that improve the speed and efficiency of training and testing neural networks.
For electrical chips, including most AI accelerators, there is a theoretical minimum limit for energy consumption. Recently, MIT researchers have started developing photonic accelerators for optical neural networks. These chips perform orders of magnitude more efficiently, but they rely on some bulky optical components that limit their use to relatively small neural networks.
In a paper published in Physical Review X, MIT researchers describe a new photonic accelerator that uses more compact optical components and optical signal-processing techniques, to drastically reduce both power consumption and chip area. That allows the chip to scale to neural networks several orders of magnitude larger than its counterparts.
To read more, click here.