As data being produced by IoT applications continues to explode, there is a
growing need to bring computing power closer to the source of the data to meet
the response time, power dissipation and cost goals of performance-critical
applications in various domains like the Industrial Internet of Things (IIoT),
Automated Driving, Medical Imaging or Surveillance among others. This paper
proposes a 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 an edge platform. In the implementation part, we
show the benefits of FPGA-based platform for the task of object detection.
Furthermore, 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
