Reviewing and Discussing Graph Reduction in Edge Computing Context

Much effort has been devoted to transferring efficiently different machine-learning algorithms, and especially deep neural networks, to edge devices in order to fulfill, among others, real-time, storage and energy-consumption issues. The limited resources of edge devices and the necessity for energy saving to lengthen the durability of their batteries, has encouraged an interesting trend in reducing neural networks and graphs, while keeping their predictability almost untouched. In this work, an alternative to the latest techniques for finding these reductions in networks size is proposed, seeking to figure out a simplistic way to shrink networks while maintaining, as far as possible, their predictability testing on well-known datasets

Graph Based Learning for Building Prediction in Smart Cities

Anticipating pedestrians’ activity is a necessary task for providing a safe and energy efficient environment in an urban area. By locating strategically sensors throughout the city useful information could be obtained. By knowing the average activity of those throughout different days of the week we could identify the typology of the buildings neighboring those sensors. For these type of purposes, clustering methods show great capability forming groups of items that have great similarity intra clusters and dissimilarity inter cluster. Different approaches are made to classify sensors depending on the typology of buildings surrounding them and the mean pedestrians’ counts for different time intervals. By this way, sensors could be classified in different groups according to their activation patterns and the environment in which they are located through clustering processes and using graph convolutional networks. This study reveals that there is a close relationship between the activity pattern of the pedestrians’ and the type of environment sensors that collect pedestrians’ data are located. By this way, institutions could alleviate a great amount of effort needed to ensure safe and energy efficient urban areas, only knowing the typology of buildings of an urban zone

SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction

Application of deep neural networks (DNN) in edge computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. To this end, shredding these original structures is urgent due to the high number of parameters needed to represent them. As a consequence, the most representative components of different layers are kept in order to maintain the network’s accuracy as close as possible to the entire network’s ones. To do so, two different approaches have been developed in this work. First, the Sparse Low Rank Method (SLR) has been applied to two different Fully Connected (FC) layers to watch their effect on the final response, and the method has been applied to the latest of these layers as a duplicate. On the contrary, SLRProp has been proposed as a variant case, where the relevances of the previous FC layer’s components were weighed as the sum of the products of each of these neurons’ absolute values and the relevances of the neurons from the last FC layer that are connected with the neurons from the previous FC layer. Thus, the relationship of relevances across layer was considered. Experiments have been carried out in well-known architectures to conclude whether the relevances throughout layers have less effect on the final response of the network than the independent relevances intra-layer