Flet-Edge: A Full Life-cycle Evaluation Tool for deep learning framework on the Edge

Xiaoyang Jiang, Xingzhou Zhang, Xiaohui Peng

Abstract: Deep learning frameworks, such as TensorFlow, PyTorch, MXNet, and Paddle Paddle are widely used and studied by industry. At the same time, AIoT (Artificial Intelligence and Internet of Things) and edge computing have provided more deep learning scenarios on the edge. In order to develop and deploy AIoT applications, we need to evaluate deep learning frameworks from ease-of-use and performance. To describe the full life-cycle performance of deep learning frameworks on the edge, this paper proposed a metric set, PDR, includes three comprehensive submetrics: Programming complexity, Deployment complexity, and Runtime performance. Based on the PDR, this paper designed and implemented a full life-cycle evaluation tool, Flet-Edge, which can automatically collect and present the PDR’s metrics, visually. Finally, to verify the availability of the Flet-Edge, this paper built a heterogeneous edge device cluster and carried out three case studies. With only one configuration file as input, the FletEdge will collect the twelve metrics of training or inference tasks and output them in text or chart. By observing the hierarchical roofline diagram provided by the Flet-Edge, this paper shows that the Flet-edge has the ability to optimize software and hardware of deep learning.

External IDs:dblp:conf/icpads/JiangZP22