HW-Accelerated Physical AI Framework For Resource-Constrained Edge Devices (ASU)

A new technical paper titled “Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics” was published by researchers at Arizona State University. Abstract “Physical AI at the edge—enabling autonomous systems to understand and predict real-world dynamics in realtime—demands efficient hardware acceleration. Model recovery (MR), which extracts governing equations from sensor data, is critical... » read more
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