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Scalable Smartphone Cluster for Deep Learning

arXiv 2110.12172 ↗First posted 23 Oct 2021cs.LG cs.DCcited by 0

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Abstract (arXiv)

Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single smartphone. A portable cluster, which connects smartphones with a wireless network and supports parallel computation using them, can be a potential approach to resolve the issue. However, by our findings, the limitations of wireless communication restrict the cluster size to up to 30 smartphones. Such small-scale clusters have insufficient computational power to train DNNs from scratch. In this paper, we propose a scalable smartphone cluster enabling deep learning training by removing the portability to increase its computational efficiency. The cluster connects 138 Galaxy S10+ devices with a wired network using Ethernet. We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library. The smartphone cluster yielded 90% of the speed of a P100 when training ResNet-50, and approximately 43x speed-up of a V100 when training MobileNet-v1.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 23 Oct 2021.

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13 of 27 references.

References · 13

  1. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  3. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
  4. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  5. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  6. 2017Channel Pruning for Accelerating Very Deep Neural Networkscited by 702
  7. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 426
  8. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 295
  9. 2014Deep learning with Elastic Averaging SGDcited by 90
  10. 2018A First Look at Deep Learning Apps on Smartphonescited by 47
  11. 2017Quantized Memory-Augmented Neural Networkscited by 3
  12. 2018Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGAcited by 3
1 more
  1. 2019Power and Thermal Analysis of Commercial Mobile Platforms: Experiments and Case Studiescited by 1

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