Compact and Fast Machine Learning Accelerator for IoT Devices

Hantao Huang, Hao Yu

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Springer Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika

Beschreibung

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.

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Schlagwörter

Shadow Neural Network, Machine Learning Accelerator, algorithm level optimization, Deep Neural Network, Internet-of-things (IoT), Tensor-solver, neural network compression, Least-squares-solver, Distributed-solver, hardware architecture optimization, Networked Neural Network