  As data being produced by IoT applications continues to explode, there's a
growing need to bring computing power closer to the source of the data to meet
the response-time, power-consumption and cost goals of performance-critical
applications like Industrial Internet of Things (IIoT), Automated Driving,
Medical Imaging or Surveillance among others. This paper proposes a FPGA-based
data collection and utilization framework that allows runtime platform and
application data to be sent to an edge and cloud system via data collection
agents running close to the platform. Agents are connected to a cloud system
able to train AI models to improve overall energy efficiency of an AI
application executed on a FPGA-based edge platform. In the implementation part
we show that it is feasible to collect relevant data from an FPGA platform,
transmit the data to a cloud system for processing and receiving feedback
actions to execute an edge AI application energy efficiently. As future work we
foresee the possibility to train, deploy and continuously improve a base model
able to efficiently adapt the execution of edge applications.
