With the increasing reliance of users on smart devices, bringing essential computation at the edge has become a crucial requirement for any type of business. Many such computations utilize Convolution Neural Networks (CNNs) to perform AI tasks, having high resource and computation requirements, that are infeasible for edge devices. Splitting the CNN architecture to perform part of the computation on edge and remaining on the cloud is an area of research that has seen increasing interest in the field. In this paper, we assert that running CNNs between an edge device and the cloud is synonymous to solving a resource-constrained optimization problem that minimizes the latency and maximizes resource utilization at the edge. We formulate a multi-objective optimization problem and propose the LMOS algorithm to achieve a Pareto efficient solution. Experiments done on real-world edge devices show that, LMOS ensures feasible execution of different CNN models at the edge and also improves upon existing state-of-the-art approaches.
翻译:随着用户日益依赖智能设备,在边缘进行基本计算已成为任何类型业务的关键要求。 许多此类计算利用进化神经网络(CNNs)执行人工智能任务,拥有高资源和计算要求,对边缘设备来说是行不通的。 将CNN架构分割成在边缘进行部分计算并留在云层上是一个研究领域,对实地的兴趣日益浓厚。 在本文中,我们断言,在边缘装置和云层之间运行有线电视新闻网是解决资源限制的优化问题的同义词,它会最大限度地减少悬崖层,最大限度地利用边缘的资源。我们提出了一个多目标优化问题,并提出LMOS算法,以实现Pareto高效的解决方案。在现实世界边缘装置上进行的实验表明,LMOS确保了在边缘执行不同的CNN模型的可行性,并改进了现有的最先进的方法。