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.
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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