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For AI at the edge, it’s go fast or go home. Arm’s ML is built for speed

Authored on: Sep 12, 2019

Technical Paper

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Good things happen if you can put artificial intelligence at the network edge. Pushing AI to the edge means a lot of the processing resources have to be right there at the edge too. That almost always means machine learning, but doing ML at the edge isn’t easy. It requires a nearly perfect balance of performance and minimal energy consumption to make it feasible at an affordable price. Arm’s ML strikes that balance, paving the way for the spread of AI-based applications.

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