Compact and Fast Machine Learning Accelerator for IoT Devices

1. Auflage 2019
ISBN: 978-981-13-3322-4
Verlag: Springer Nature Singapore

Buch, Englisch, 149 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 412 g

Reihe: Computer Architecture and Design Methodologies

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.

Zielgruppe

Research

Autoren/Hrsg.

Yu, Hao

Huang, Hantao

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

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.