AI Needs Enormous Computing Power. Could Light-Based Chips Help? | Quanta Magazine
The researchers taught their experimental device to recognize spoken vowels, a common benchmark task for neural networks. With the advantages of light, it could do so faster and more efficiently than an electronic device. Other researchers had known that light had the potential to be good for matrix multiplication; the 2017 paper showed how to put it into practice.
The study “catalyzed massive, renewed interest in ONNs,” said Peter McMahon, a photonics expert at Cornell University. “That one has...
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