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

17-10-2019 | Review Article

Implementation of DNNs on IoT devices

Authors: Zhichao Zhang, Abbas Z. Kouzani

Published in: Neural Computing and Applications | Issue 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.
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Metadata
Title
Implementation of DNNs on IoT devices
Authors
Zhichao Zhang
Abbas Z. Kouzani
Publication date
17-10-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04550-w

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Neural Computing and Applications 5/2020 Go to the issue

S.I. : AI and ML applied to Health Sciences (MLHS)

An artificial neural network model for the prediction of bruxism by means of occlusal variables

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A classifier of matrix modular neural network to simplify complex classification tasks

S.I.: AI and ML applied to Health Sciences (MLHS)

Hybrid model for the ANI index prediction using Remifentanil drug and EMG signal

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An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

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Sparse representation and overcomplete dictionary learning for anomaly detection in electrocardiograms

S.I. : AI and ML applied to Health Sciences (MLHS)

A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks

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