Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture – Ebook PDF Instant Delivery – ISBN(s): 9780323857833,0323857833
Product details:
- ISBN-10 : 0323857833
- ISBN-13 : 978-0323857833
- Author:
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.
Table contents:
Part 1: Introduction
Chapter 1 – Introduction
Chapter 2 – The basics of deep learning
Part 2: Model and algorithm
Chapter 3 – Model design and compression
Chapter 4 – Mix-precision model encoding and quantization
Chapter 5 – Model encoding of binary neural networks
Part 3: Architecture optimization
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
People also search:
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture
What are edge devices in deep learning
How is machine learning used in edge computing
What is DNN in edge computing
What devices are using edge computing