The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective

Lucas Caetano Pereira, Bruna Guterres, Amanda Mendes, Francisca Vermeulen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.
Original languageEnglish
Title of host publicationSAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Print)978-1-4503-9517-5
Publication statusPublished - 7 Jun 2023


  • artificial intelligence
  • performance and energy efficiency
  • edge computing
  • edge-based deep learning


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