DC6 Project: GPU-accelerated Edge computing for Federated Learning reasoning in industrial environments    

PhD Supervisor: prof. Dariusz Mrozek (SUT); Auxiliary supervisors: Francesco Piccialli (UNINA), David Camacho (UPM), Jia-Chun Lin (NTNU); R&D cooperation: CONTI

Objectives: To develop Federated Learning (FL) models with direct GPU acceleration for training, updating and testing the prediction and reasoning processes that are performed at the Edge of a network. To develop GPU-based methods for encoding and deriving features from industrial data streams and transferring the local FL models that are built. 

Expected Results: A novel programmatic library for AI-based inferencing within the distributed industrial environment of AGV vehicles with Edge modules that operate on automated production lines. Decreasing the amount of data that is sent in industrial environments that requires early anomaly detection and fault prediction via AI-based reasoning. Accelerating the detection and prediction processes to support real-time predictive maintenance in smart manufacturing.

Applied research: Edge IoT services for effectively processing the sensor data by GPU-based processing including the data accumulators used in the event of a network disconnection will be verified by industrial projects proposed by CONTI  with focus the various approaches, multi-threading arrangements and GPU memory types for the optimal execution of the analytical processes for industrial data.

Planned secondments: UNINA(4 months); UPM(4 months); NTNU (4 months)                   

Enrolment in Doctoral degree: SUT

Recruitment of Doctoral Candidate: https://euraxess.ec.europa.eu/jobs/28797