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Fundamentals and Literature Review

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Book cover Compact and Fast Machine Learning Accelerator for IoT Devices

Part of the book series: Computer Architecture and Design Methodologies ((CADM))

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Abstract

In this chapter, edge computing on IoT devices is firstly discussed to achieve low-latency, energy efficient, private and scalable computation. Then we use IoT based smart buildings as one example to illustrate the edge computing in IoT system for applications such as indoor positioning, energy management and network intrusion detection. Furthermore, we will discuss the basics of the machine learning algorithms, distributed machine learning, machine learning accelerators and machine learning model optimizations. A comprehensive literature review on distributed and compact machine learning algorithms is also provided.

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Notes

  1. 1.

    Obviously, the occupant refers to the end users of buildings.

  2. 2.

    Some literatures may indicate the inference process as the testing process. In this book, testing and inference are interchangeable and testing data refers to the data used for inference.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Hantao Huang

  2. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

    Hao Yu

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  1. Hantao Huang
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  2. Hao Yu
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Correspondence to Hantao Huang .

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Huang, H., Yu, H. (2019). Fundamentals and Literature Review. In: Compact and Fast Machine Learning Accelerator for IoT Devices. Computer Architecture and Design Methodologies. Springer, Singapore. https://doi.org/10.1007/978-981-13-3323-1_2

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