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Compact and Fast Machine Learning Accelerator for IoT Devices

  • Hantao Huang
  • Hao Yu

Part of the Computer Architecture and Design Methodologies book series (CADM)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Hantao Huang, Hao Yu
    Pages 1-8
  3. Hantao Huang, Hao Yu
    Pages 9-28
  4. Hantao Huang, Hao Yu
    Pages 29-62
  5. Hantao Huang, Hao Yu
    Pages 63-105
  6. Hantao Huang, Hao Yu
    Pages 107-143
  7. Hantao Huang, Hao Yu
    Pages 145-149

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • Hantao Huang
    • 1
  • Hao Yu
    • 2
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhenChina

Bibliographic information