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A research team has developed new optical computing hardware for AI and machine learning that is faster and much more energy-efficient while addressing the noise inherent to optical computing that can interfere with computing precision.
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Artificial intelligence and machine learning are already an integral part of our everyday lives online. For example, search engines such as Google use intelligent ranking algorithms and video streaming services such as Netflix use machine learning to personalize movie recommendations.
As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption.
Now a University of Washington-led team has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing.