Chip Design Drastically Reduces Energy Needed To Compute With Light

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.

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