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 E-Mail IMAGE: Technology developed through a KAUST-led collaboration with Intel, Microsoft and the University of Washington can dramatically increase the speed of machine learning on parallelized computing systems. view more  Credit: © 2021 KAUST; Anastasia Serin. Inserting lightweight optimization code in high-speed network devices has enabled a KAUST-led collaboration to increase the speed of machine learning on parallelized computing systems five-fold. This in-network aggregation technology, developed with researchers and systems architects at Intel, Microsoft and the University of Washington, can provide dramatic speed improvements using readily available programmable network hardware. The fundamental benefit of artificial intelligence (AI) that gives it so much power to understand and interact with the world is the machine-learning step, in which the model is trained using large sets of labeled training data. The more data the AI is trained on, the better t

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