SplitPlace: intelligent placement of split neural nets in mobile edge environments
File(s)
Working paper
Author(s)
Tuli, Shreshth
Type
Working Paper
Abstract
In recent years, deep learning models have become ubiquitous in industry and
academia alike. Modern deep neural networks can solve one of the most complex
problems today, but coming with the price of massive compute and storage
requirements. This makes deploying such massive neural networks challenging in
the mobile edge computing paradigm, where edge nodes are resource-constrained,
hence limiting the input analysis power of such frameworks. Semantic and
layer-wise splitting of neural networks for distributed processing show some
hope in this direction. However, there are no intelligent algorithms that place
such modular splits to edge nodes for optimal performance. This work proposes a
novel placement policy, SplitPlace, for the placement of such neural network
split fragments on mobile edge hosts for efficient and scalable computing.
academia alike. Modern deep neural networks can solve one of the most complex
problems today, but coming with the price of massive compute and storage
requirements. This makes deploying such massive neural networks challenging in
the mobile edge computing paradigm, where edge nodes are resource-constrained,
hence limiting the input analysis power of such frameworks. Semantic and
layer-wise splitting of neural networks for distributed processing show some
hope in this direction. However, there are no intelligent algorithms that place
such modular splits to edge nodes for optimal performance. This work proposes a
novel placement policy, SplitPlace, for the placement of such neural network
split fragments on mobile edge hosts for efficient and scalable computing.
Date Issued
2021-10-10
Citation
2021
Publisher
arXiv
Copyright Statement
© 2021 The Author(s). This work is published under CC BY license.
License URL
Identifier
http://arxiv.org/abs/2110.04841v1
Subjects
cs.DC
cs.DC
Notes
First Place - Gold Medal at the Student Research Competition at ACM SIGMETRICS Conference 2021
Publication Status
Published