284,21 €
315,79 €
-10% with code: EXTRA
Compact and Fast Machine Learning Accelerator for Iot Devices
Compact and Fast Machine Learning Accelerator for Iot Devices
284,21
315,79 €
  • We will send in 10–14 business days.
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 syste…
  • Publisher:
  • ISBN-10: 981133322X
  • ISBN-13: 9789811333224
  • Format: 15.6 x 23.4 x 1.1 cm, hardcover
  • Language: English
  • SAVE -10% with code: EXTRA

Compact and Fast Machine Learning Accelerator for Iot Devices (e-book) (used book) | bookbook.eu

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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.

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  • Author: Hantao Huang
  • Publisher:
  • ISBN-10: 981133322X
  • ISBN-13: 9789811333224
  • Format: 15.6 x 23.4 x 1.1 cm, hardcover
  • Language: English English

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.

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