Data collection and utilization framework for edge AI applications

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

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 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 a 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.

Original languageEnglish
Title of host publication2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)
PublisherIEEE
Pages105-108
Number of pages4
ISBN (Print)978-1-6654-4471-2
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventIEEE/ACM Workshop on AI Engineering - Software Engineering for AI -
Duration: 30 May 202131 May 2021

Conference

ConferenceIEEE/ACM Workshop on AI Engineering - Software Engineering for AI
Abbreviated titleWAIN
Period30/05/2131/05/21

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