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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2020

17.10.2019 | Review Article

Implementation of DNNs on IoT devices

verfasst von: Zhichao Zhang, Abbas Z. Kouzani

Erschienen in: Neural Computing and Applications | Ausgabe 5/2020

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Abstract

Driven by the recent growth in the fields of internet of things (IoT) and deep neural networks (DNNs), DNN-powered IoT devices are expected to transform a variety of industrial applications. DNNs, however, involve many parameters and operations to process the data generated by IoT devices. This results in high data-processing latency and energy consumption. New approaches are thus being souhgt to tackle these issues and deploy real-time DNNs into resource-limited IoT devices. This paper presents a comprehensive review on hardware-and-software-co-design approaches developed to implement DNNs on low-resource hardware platforms. These approaches explore the trade-off between energy consumption, speed, classification accuracy, and model size. First, an overview of DNNs is given. Next, available tools for implementing DNNs on low-resource hardware platforms are described. Then, the memory hierarchy designs together with dataflow mapping strategies are presented. Furthermore, various model optimization approaches, including pruning and quantization, are discussed. In addition, case studies are given to demonstrate the feasibility of implementing DNNs for IoT applications. Finally, detailed discussions, research gaps, and future directions are provided. The presented review can guide the design and implementation of the next generation of hardware and software solutions for real-world IoT applications.
Vorheriger Artikel Improved GWO for large-scale function optimization and MLP optimization in cancer identification
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Metadaten
Titel
Implementation of DNNs on IoT devices
verfasst von
Zhichao Zhang
Abbas Z. Kouzani
Publikationsdatum
17.10.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04550-w

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