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.

翻译：在智能手机上的各种深层次学习应用一直在迅速增加,但培训深层神经网络(DNN)的计算负担太重,无法在一个智能手机上执行。将智能手机与无线网络连接起来并支持使用这些网络的平行计算的便携式集束可能是解决问题的一个潜在办法。然而,根据我们的研究结果,无线通信的局限性将集群的大小限制在30个智能手机上。这些小型集束没有足够从零开始培训DN的计算能力。在本文中,我们提议建立一个可变智能电话集群,通过去除可移动性提高其计算效率,进行深层学习培训。集束将138Galaxy S10+装置与使用Ethernet的有线网络连接起来。我们根据一个深层学习图书馆Caffe对DNes进行了大型同步培训。智能集在培训ResNet-50时,P100速度达90%。在培训移动网络1时,智能聚束使V100速度达到约43x速度。