Offers readers a systematic and comprehensive literature review of fast and compact machine learning algorithms on IoT devices
Provides various techniques on neural network model optimization such as bit-width truncation and matrix (tensor) decomposition
Focuses on machine learning architecture design on both CMOS technology and RRAM technology to provide energy-efficient hardware solutions
Illustrates design and analysis for real-life applications such as indoor positioning, energy management and network security in smart buildings
Offers readers a systematic and comprehensive literature review of fast and compact machine learning algorithms on IoT devices
Provides various techniques on neural network model optimization such as bit-width truncation and matrix (tensor) decomposition
Focuses on machine learning architecture design on both CMOS technology and RRAM technology to provide energy-efficient hardware solutions
Illustrates design and analysis for real-life applications such as indoor positioning, energy management and network security in smart buildings
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.
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.
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.
Inhaltsverzeichnis
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.
Klappentext
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.
Offers readers a systematic and comprehensive literature review of fast and compact machine learning algorithms on IoT devices
Provides various techniques on neural network model optimization such as bit-width truncation and matrix (tensor) decomposition
Focuses on machine learning architecture design on both CMOS technology and RRAM technology to provide energy-efficient hardware solutions
Illustrates design and analysis for real-life applications such as indoor positioning, energy management and network security in smart buildings
