AI-Processor Electronics: Basic technology of artificial intelligence

The revolutionary impact of advances in artificial intelligence on consumer and industrial electronics has significantly increased the demand for specialized processing units for AI workloads. AI-Processor Electronics: Basic Technology of Artificial Intelligence explores the principles and technology behind these processors, which perform fast, energy-efficient data processing and complex computations. The book begins with an overview of AI, machine learning, and deep learning, and a brief introduction to digital computer electronics. It highlights the limitations of traditional CPUs in modern AI, discussing parallel computing and AI-optimized hardware that are specially tailored and adapted to meet AI requirements. The book then covers various AI processors, including graphical, tensor, neural, convolutional neural network, vision, sparse neural network, graph analytics, associative memory, and quantum processing units.

Key features:

  • Provides a review of the physics of operation of a diverse variety of representative AI processors, and the key concepts and innovations underlying their development
  • Covers the underlying principles, technologies, opportunities and future prospects
  • Highlights the inadequacies of general-purpose processors in meeting the computationally intensive needs of artificial intelligence workloads
  • Elucidates the use of parallel processing architecture in simultaneously performing multiple calculations across data streams
  • Explores the concepts of neuromorphic and quantum computing to address the growing demands of big data in AI
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AI-Processor Electronics: Basic technology of artificial intelligence

The revolutionary impact of advances in artificial intelligence on consumer and industrial electronics has significantly increased the demand for specialized processing units for AI workloads. AI-Processor Electronics: Basic Technology of Artificial Intelligence explores the principles and technology behind these processors, which perform fast, energy-efficient data processing and complex computations. The book begins with an overview of AI, machine learning, and deep learning, and a brief introduction to digital computer electronics. It highlights the limitations of traditional CPUs in modern AI, discussing parallel computing and AI-optimized hardware that are specially tailored and adapted to meet AI requirements. The book then covers various AI processors, including graphical, tensor, neural, convolutional neural network, vision, sparse neural network, graph analytics, associative memory, and quantum processing units.

Key features:

  • Provides a review of the physics of operation of a diverse variety of representative AI processors, and the key concepts and innovations underlying their development
  • Covers the underlying principles, technologies, opportunities and future prospects
  • Highlights the inadequacies of general-purpose processors in meeting the computationally intensive needs of artificial intelligence workloads
  • Elucidates the use of parallel processing architecture in simultaneously performing multiple calculations across data streams
  • Explores the concepts of neuromorphic and quantum computing to address the growing demands of big data in AI
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AI-Processor Electronics: Basic technology of artificial intelligence

AI-Processor Electronics: Basic technology of artificial intelligence

by Vinod Kumar Khanna
AI-Processor Electronics: Basic technology of artificial intelligence

AI-Processor Electronics: Basic technology of artificial intelligence

by Vinod Kumar Khanna

eBook

$159.00 

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Overview

The revolutionary impact of advances in artificial intelligence on consumer and industrial electronics has significantly increased the demand for specialized processing units for AI workloads. AI-Processor Electronics: Basic Technology of Artificial Intelligence explores the principles and technology behind these processors, which perform fast, energy-efficient data processing and complex computations. The book begins with an overview of AI, machine learning, and deep learning, and a brief introduction to digital computer electronics. It highlights the limitations of traditional CPUs in modern AI, discussing parallel computing and AI-optimized hardware that are specially tailored and adapted to meet AI requirements. The book then covers various AI processors, including graphical, tensor, neural, convolutional neural network, vision, sparse neural network, graph analytics, associative memory, and quantum processing units.

Key features:

  • Provides a review of the physics of operation of a diverse variety of representative AI processors, and the key concepts and innovations underlying their development
  • Covers the underlying principles, technologies, opportunities and future prospects
  • Highlights the inadequacies of general-purpose processors in meeting the computationally intensive needs of artificial intelligence workloads
  • Elucidates the use of parallel processing architecture in simultaneously performing multiple calculations across data streams
  • Explores the concepts of neuromorphic and quantum computing to address the growing demands of big data in AI

Product Details

ISBN-13: 9780750362597
Publisher: Institute of Physics Publishing
Publication date: 01/29/2025
Series: IOP ebooks
Sold by: Barnes & Noble
Format: eBook
Pages: 400
File size: 27 MB
Note: This product may take a few minutes to download.

About the Author

Vinod Kumar Khanna, Ph.D. (Physics), is an independent researcher at Chandigarh, India; a former emeritus scientist, Council of Scientific and Industrial Research (CSIR), India, and Emeritus Professor, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; a retired chief scientist, CSIR-Central Electronics Engineering Research Institute, Pilani, India and professor, AcSIR, India. He has worked for more than 37 years on the design, fabrication, and characterization of power semiconductor devices, MEMS, and nanotechnology-based sensors. He has published 194 research papers in refereed journals and conference proceedings, 21 books, and 6 chapters in edited books. He has five patents to his credit, two US and three Indian.


Vinod Kumar Khanna is a former Emeritus Scientist at CSIR-Central Electronics Engineering Research Institute, Pilani, India, and Emeritus Professor at the Academy of Scientific&Innovative Research, India. He is a retired Chief Scientist and Head of the MEMS&Microsensors Group, CSIR-CEERI, Pilani.

Table of Contents

Chapter 1: Artificial intelligence, machine learning, deep learning and generative artificial intelligence

Chapter 2: Computing electronics fundamentals

Chapter 3: Central processing unit and the von Neumann bottleneck

Chapter 4: Parallel computing architecture

Chapter 5: Optimised AI-computing within physical limites of transitors

Chapter 6: Graphical processing unit

Chapter 7: Tensor processing unit

Chapter 8: Neural processing unit

Chapter 9: Convolution neural network processor, and the vision processing unit

Chapter 10: Conpressed and sparse neural network processors

Chapter 11: Graph analytics processor for graph algorithm computations

Chapter 12: Associative processing unit

Chapter 13: Quantum computing principles and devices

Chapter 14: Quantum logic gates and circuits

Chapter 15: Quantum processing unit

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