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What’s Powering Artificial Intelligence?

How hardware and software developers can enable AI/ML performance across a vast array of devices and still balance functionality alongside security, affordability, complexity and general compute needs.

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While artificial intelligence (AI) and machine learning (ML) applications soar in popularity, many organizations are questioning where ML workloads should be performed. Should they be done on a central processor (CPU), a graphics processor (GPU), or a neural processor (NPU)? The choice most teams are making today will surprise you.

To scale artificial intelligence (AI) and machine learning (ML), hardware and software developers must enable AI/ML performance across a vast array of devices. This requires balancing the need for functionality alongside security, affordability, complexity and general compute needs. Fortunately, there’s a solution hiding in plain sight.

By Rene Haas, President, IPG Group, and Jem Davies, GM of Machine Learning, Arm.

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