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Researchers develop optical computing for AI addressing noise problem

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. ....

Mo Li , Changming Wu , Ruoming Peng , Xiaoxuan Yang , Ichiro Takeuchi , Yiran Chen , Heshan Yu , College Park , Generative Adversarial Network , Office Of Naval Research , University Of Maryland , Army Research Office , National Science Foundation , Duke University , Science Advances , Generative Adversarial , Naval Research , Army Research , Washington Nanofabrication Facility , Ews India ,

Harnessing noise in optical computing for AI

Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning ....

Mo Li , Changming Wu , Ruoming Peng , Xiaoxuan Yang , Ichiro Takeuchi , Yiran Chen , Heshan Yu , College Park , Office Of Naval Research , University Of Maryland , National Science Foundation , Generative Adversarial Network , University Of Washington , Army Research Office , Duke University , Science Advances , Generative Adversarial , Naval Research , Army Research , Washington Nanofabrication Facility ,

Accelerating AI computing to the speed of light

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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. ....

Mo Li , Changming Wu , Ruoming Peng , Ichiro Takeuchi , Heshan Yu , Seokhyeong Lee , Nature Communications , University Of Maryland , Washington Nanofabrication , Molecular Analysis Facility , Technology Engineering Computer Science , Computer Science , Multimedia Networking Interface Design , Electrical Engineering Electronics , மோ லி , ஹேஷன் யூ , இயற்கை தகவல்தொடர்புகள் , பல்கலைக்கழகம் ஆஃப் மேரிலாந்து , மூலக்கூறு பகுப்பாய்வு வசதி , தொழில்நுட்பம் பொறியியல் கணினி அறிவியல் , கணினி அறிவியல் , மல்டிமீடியா நெட்வொர்க்கிங் இடைமுகம் வடிவமைப்பு , மின் பொறியியல் மின்னணுவியல் ,