With the prosperity of mobile devices, the distributed learning approach enabling model training with decentralized data has attracted wide research. However, the lack of training capability for edge devices significantly limits the energy efficiency of distributed learning in real life. This paper describes a novel approach of training DNNs exploiting the redundancy and the weight asymmetry potential of conventional backpropagation. We demonstrate that with negligible classification accuracy loss, the proposed approach outperforms the prior arts by 5x in terms of energy efficiency.

中文翻译：

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边缘修剪的符号对称反馈对齐在边缘设备上的高效训练卷积神经网络

随着移动设备的繁荣，使得能够利用分散数据进行模型训练的分布式学习方法吸引了广泛的研究。但是，缺乏对边缘设备的培训能力，极大地限制了现实生活中分布式学习的能源效率。本文介绍了一种利用传统反向传播的冗余和权重不对称潜力训练DNN的新方法。我们证明，在分类精度损失可忽略不计的情况下，所提出的方法在能效方面比现有技术高出5倍。