Keeping AI hardware current and relevant is becoming a challenge.
Figuring out the best kinds of processors to use for different AI workloads is a challenge. AI algorithms are undergoing rapid and frequent changes, and the workloads tied to them can vary by data type, by user, and sometimes because of software/firmware updates. On top of that, AI computations tend to require much higher utilization rates than traditional computing, and that will only become more pronounced with agentic and physical AI. Amol Borkar, senior director of product management and head of computer vision/AI products at Cadence, talks about the growing role for AI co-processors, how they compare to other types of processors such as CPUs, GPUs, and DSPs, and when and where to use them in multi-die systems.
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