Deep learning on edge computing devices : design challenges of algorithm and architecture /

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. --

Bibliographic Details
Main Authors:	Zhou, Xichuan (Author), Liu, Haijun (Author), Shi, Cong (Author), Liu, Ji (Author)
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	Amsterdam ; Cambridge, MA : Elsevier, [2022]
Series:	ITpro collection
Subjects:	Deep learning (Machine learning) Edge computing. Operating systems (Computers) Apprentissage profond. Systèmes d'exploitation (Ordinateurs) operating systems. Edge computing Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Part 1. Introduction ; 1. Introduction; ; Part 2. Theory and Algorithm ; 2. Model Inference on Edge Device; 3. Model Training on Edge Device; 4. Network Encoding and Quantization; ; Part 3. Architecture Optimization ; 5. DANoC: An Algorithm and Hardware Codesign Prototype; 6. Ensemble Spiking Networks on Edge Device; 7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype