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