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Offloaded inference for real-world physical AI robotics
Offloading robotics inference improves performance and battery life
Microsoft Research presents a systematic study of robotics inference workloads showing that offloading AI inference from onboard GPUs to edge or cloud infrastructure improves task success rates, enables larger models, and extends battery life by up to 160% compared to onboard GPU compute.
The research introduces a Kubernetes-based toolset for containerizing and orchestrating distributed robotics AI workloads across robots, edge infrastructure, and the cloud.
From the source
Offloading physical AI inference out of the robot improved its response time and accuracy, along with battery lifetime and cost. We evaluated the inference models across a range of onboard, edge, and cloud compute configurations.
microsoft.com
