Reviewing and Discussing Graph Reduction in Edge Computing Context

Asier Garmendia-Orbegozo*, José David Núñez-Gonzalez, Miguel Ángel Antón

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Much effort has been devoted to transferring efficiently different machine-learning algorithms, and especially deep neural networks, to edge devices in order to fulfill, among others, real-time, storage and energy-consumption issues. The limited resources of edge devices and the necessity for energy saving to lengthen the durability of their batteries, has encouraged an interesting trend in reducing neural networks and graphs, while keeping their predictability almost untouched. In this work, an alternative to the latest techniques for finding these reductions in networks size is proposed, seeking to figure out a simplistic way to shrink networks while maintaining, as far as possible, their predictability testing on well-known datasets.

    Original languageEnglish
    Article number161
    JournalComputation
    Volume10
    Issue number9
    DOIs
    Publication statusPublished - Sept 2022

    Keywords

    • artificial intelligence
    • edge computing
    • graph reduction
    • pruning

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