Physical AI at the Edge: Connecting Perception to Action for On-Device Autonomous Intelligence by Tinoosh Mohsenin

As AI systems move beyond perception to enable real-time, reliable action in the physical world, their computational demands continue to rise due to the rapidly growing volume of data and increasingly complex algorithms required for higher-quality results. At the same time, while these systems demand energy efficiency and real-time performance, they often rely on cloud infrastructure, introducing latency, network dependence, and privacy risks.

In this talk, I will discuss hardware-aware architectures and the co-design of algorithms and systems that enable efficient, real-time intelligence directly on-device. I will also present metareasoning approaches that dynamically balance computation between edge and cloud to optimize real-time performance and energy efficiency. I will conclude with a forward-looking perspective on the opportunities and challenges in building scalable, deployable Physical AI systems that are efficient, resilient, and ready for real-world impact.