Novel smart environments, such as smart home, smart city, and intelligent transportation, are driving increasing interest in deploying deep neural networks (DNN) at edge devices. Unfortunately, deploying DNN on resource-constrained edge devices poses a huge challenge. If a simulator can interact with deep learning frameworks, it can facilitate researches on deep learning at edge. The existing simulation frameworks, such as Matlab, NS-3, etc., haven't been extended to support simulations of edge learning. To support large-scale training simulations on edge nodes, we propose a discrete-event-based edge learning simulator. It includes a deep learning module and a network simulation module. Specifically, it enable simulations as an environment for deep learning. Our framework is generic and can be used in various deep learning problems before the deep learning model is deployed. In this paper, we give the design and implementation details of the discrete-event-based learning simulator and present an illustrative use case of the proposed simulator.

翻译：智能家庭、智能城市和智能交通等智能智能环境正在促使人们越来越有兴趣在边缘装置上部署深神经网络。 不幸的是,在资源限制的边缘装置上部署 DNN 是一个巨大的挑战。 如果模拟器能够与深学习框架互动,它可以促进边缘深学习的研究。 现有的模拟框架,如Matlab、NS-3等,还没有扩大到支持边缘学习模拟。 为了支持边缘节点上的大规模培训模拟,我们建议使用一个基于远视活动的边缘学习模拟器。 它包括一个深学习模块和一个网络模拟模块。 具体地说,它使得模拟成为深学习的环境。 我们的框架是通用的,可以在深学习模型部署之前用于各种深学习问题。 在本文中,我们给出了以离散活动为基础的学习模拟器的设计和实施细节,并展示了拟议模拟器的示例。