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Deep Learning on Edge Computing Devices - Research Book

Deep Learning on Edge Computing Devices - Research Book

Latest Research Book in Deep Learning on Edge Computing Devices

Author(s) Name:  Xichuan Zhou, Haijun Liu, Cong Shi, Ji Liu

About the Book:

   Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design.

Key Features

  • Focuses on hardware architecture and embedded deep learning, including neural networks
  • Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications
  • Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud
  • Describes how to maximize the performance of deep learning on Edge-computing devices
  • Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring

  • Table of Contents

  • Chapter 1: Introduction
  • Chapter 2: The basics of deep learning
  • Chapter 3: Model design and compression
  • Chapter 4: Mix-precision model encoding and quantization
  • Chapter 5: Model encoding of binary neural networks
  • Chapter 6: Binary neural network computing architecture
  • Chapter 7: Algorithm and hardware codesign of sparse binary network on-chip
  • Chapter 8: Hardware architecture optimization for object tracking
  • Chapter 9: SensCamera: A learning-based smart camera prototype
  • ISBN:  9780323857833

    Publisher:  Elsevier

    Year of Publication:  2022

    Book Link:  Home Page Url