论文标题
云和物联网之间的地理分布计算
Layerwise Geo-Distributed Computing between Cloud and IoT
论文作者
论文摘要
在本文中，我们为深度学习系统（名为K-Degree Layer-the Layer网络）提出了一种新颖的体系结构，以实现云和物联网（IoT）之间有效的地理分布计算。地理分布的计算将云扩展到物联网邻居中网络的地理边缘。该提案的基本思想包括k度约束和层面的约束。定义了k度约束，以使第h-then层上每个顶点的程度恰好是k（h），以扩展现有的深信信念网络并控制通信成本。定义了层的约束，以使层的程度在正方向上单调降低，以逐渐降低数据的尺寸。我们证明K度量层网络很少，而典型的深神经网络则密集。在对M分布的MNIST数据库的评估中，该提案优于沟通成本和学习时间的最先进模型，并具有可伸缩性。
In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a layer-wise constraint. The k-degree constraint is defined such that the degree of each vertex on the h-th layer is exactly k(h) to extend the existing deep belief networks and control the communication cost. The layer-wise constraint is defined such that the layer-wise degrees are monotonically decreasing in positive direction to gradually reduce the dimension of data. We prove the k-degree layer-wise network is sparse, while a typical deep neural network is dense. In an evaluation on the M-distributed MNIST database, the proposal is superior to a state-of-the-art model in terms of communication cost and learning time with scalability.
