Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges

Sudeep Pasricha, Muhammad Shafique

Research output: Book/ReportBook

Abstract

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. • Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; • Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; • Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Original languageEnglish (US)
PublisherSpringer Nature
Number of pages571
ISBN (Electronic)9783031406775
ISBN (Print)9783031406768
DOIs
StatePublished - Jan 1 2023

Keywords

  • Hardware-Aware neural architectural search
  • Machine learning edge computing
  • Machine learning embedded systems
  • Machine learning IoT
  • Smart cyber-physical systems

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering
  • General Social Sciences

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