Scaling Data Movement For Physical AI

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Physical AI systems mix multiple compute units and need to process data, make decisions in real time, and manage battery life; the key is deciding where data is processed and how to minimize data movement. Ashley Stevens, Arteris's senior director of product management and marketing, explains that coherent interconnects suit CPUs better while non-coherent interconnects suit AI accelerators better, and looks ahead at other challenges engineers will face.

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One size does not fit all.

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Physical AI poses a host of new challenges for chip engineers. These systems contain a mix of compute elements, which collectively must process a stream of real-time data, analyze it, make decisions, and still function long enough on a battery to be useful. The key here is deciding what data gets processed where, and how to minimize the movement of data. Ashley Stevens, director of product management and marketing at Arteris, talks explains why data movement plays such an important role in these systems, why coherent interconnects are better suited to CPUs and non-coherent interconnects are better for AI acceleration, and what other challenges engineers will face going forward.

Ed Sperling

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Ed Sperling is the editor in chief of Semiconductor Engineering.