Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

Date

2022

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI.

Description

https://attend.ieee.org/dsc-2022/sicsa-event/

Keywords

Edge computing, artificial intelligence, deep learning, data classification, computational offloading

Citation

Alsalemi, A. et al. (2022) Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification. IEEE DSC 2022 (5th IEEE Conference on Dependable and Secure Computing),

Rights

Research Institute

Institute of Artificial Intelligence (IAI)