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What is Pooling Layer

Smart Systems Design, Applications, and Challenges
A network layer that determines the average pooling or max pooling of a window of neurons. The pooling layer subsamples the input feature maps to achieve translation invariance and reduce over-fitting.
Published in Chapter:
Deep Learning on Edge: Challenges and Trends
Mário P. Véstias (INESC-ID, ISEL, Instituto Politécnico de Lisboa, Portugal)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/978-1-7998-2112-0.ch002
Abstract
Deep learning on edge has been attracting the attention of researchers and companies looking to provide solutions for the deployment of machine learning computing at the edge. A clear understanding of the design challenges and the application requirements are fundamental to understand the requirements of the next generation of edge devices to run machine learning inference. This chapter reviews several aspects of deep learning: applications, deep learning models, and computing platforms. The way deep learning is being applied to edge devices is described. A perspective of the models and computing devices being used for deep learning on edge are given, as well as what challenges face the hardware designers to guarantee the vast set of tight constraints like performance, power consumption, flexibility, etc. of edge computing platforms. Finally, a trends overview of deep learning models and architectures is discussed.
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Convolutional Neural Network
A network layer that determines the average pooling or max pooling of a window of neurons. The pooling layer subsamples the input feature maps to achieve translation invariance and reduce over-fitting.
Full Text Chapter Download: US $37.50 Add to Cart
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