Compact and fast machine learning accelerator for IoT devices /
General Material Designation
[Book]
First Statement of Responsibility
Hantao Huang and Hao Yu.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Singapore :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2019.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
SERIES
Series Title
Computer architecture and design methodologies
CONTENTS NOTE
Text of Note
Computing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion.
0
SUMMARY OR ABSTRACT
Text of Note
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9789811333231
OTHER EDITION IN ANOTHER MEDIUM
Title
Compact and fast machine learning accelerator for IoT devices.