TaskSense introduces a sensor language that automatically translates the capabilities and data dependencies of sensor systems into vocabularies and
grammar rules that can be understood by LLMs. It then interprets
user intentions into executable task plans for sensor systems using this sensor language in combination with LLMs.
Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput undercertain resource constraints.
EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM.