Compact and Fast Machine Learning Accelerator for IoT Devices

• Κύριοι συγγραφείς: Huang, Hantao (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Yu, Hao (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Computer Architecture and Design Methodologies,
• Θέματα: Computational intelligence.; Microprocessors.; Mathematical optimization.; Computational Intelligence.; Processor Architectures.; Optimization.
• Φυσική περιγραφή: IX, 149 p. 76 illus., 61 illus. in color. online resource.
• ISBN: 9789811333231
• ISSN: 2367-3478
• DOI: 10.1007/978-981-13-3323-1

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.