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Hardware Technologies for Artificial Intelligence: AI Chips, Ising Machines, and In-Memory Computing [Kõva köide]

  • Formaat: Hardback, 224 pages, kõrgus x laius: 234x156 mm, kaal: 600 g, 7 Tables, black and white; 122 Line drawings, black and white; 3 Halftones, black and white; 125 Illustrations, black and white
  • Ilmumisaeg: 04-Dec-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032985127
  • ISBN-13: 9781032985121
Teised raamatud teemal:
Hardware Technologies for Artificial Intelligence: AI Chips, Ising Machines, and In-Memory Computing
  • Formaat: Hardback, 224 pages, kõrgus x laius: 234x156 mm, kaal: 600 g, 7 Tables, black and white; 122 Line drawings, black and white; 3 Halftones, black and white; 125 Illustrations, black and white
  • Ilmumisaeg: 04-Dec-2025
  • Kirjastus: CRC Press
  • ISBN-10: 1032985127
  • ISBN-13: 9781032985121
Teised raamatud teemal:

In this comprehensive reference work for researchers, engineers, and students, Kawahara provides a one-stop exploration of next-generation computing at the LSI circuit level, with a focus on the integration of AI, advanced LSI design, Ising machines, and memory innovations.

While current GPUs have high parallel processing capabilities suitable for computations on large datasets, their power consumption is approaching its limit and requires further development. Additionally, edge computing is becoming increasingly important alongside cloud computing. Amid these significant technological trends, this book provides readers with important insights into next-generation computing, namely (1) neural network (artificial intelligence) LSIs and their low power and high performance, (2) hardware design technology for combinatorial optimization problems and Ising machines, and (3) semiconductor memory and data-centric computing. Kawahara first describes the basics of LSI design and neural networks before then describing their large-scale integration, power efficiency and performance enhancements. He then also explains hardware design techniques for Ising machines, offers case studies of fully coupled Ising machine LSI. Last, he discusses the basics of semiconductor memory, near/in-memory AI computing, and then examines the future prospects. Readers will be able to apply this knowledge to the design and manufacture of such devices to overcome the limitations of current hardware and computational methods, driving future advancements in artificial intelligence and optimization.

This is a valuable reference for students, engineers and researchers alike in this field. As it begins with the basics, it enables all readers to follow the direction of next-generation computing and its important technical content without the need for prior knowledge or reference to other books.



This is a valuable reference for students, engineers and researchers alike, in this field. As it begins with the basics, it enables all readers to follow the direction of next-generation computing and its important technical content without the need for prior knowledge or reference to other books.

1. Introduction: AI computing
2. Overview of artificial intelligence
hardware LSI (AI chips) and its components
3. Basics of LSI (Large Scale
Integrated Circuits) for AI
4. Basic structure of AI chips and various neural
networks
5. Low-power, high-performance AI chips structure and related
computing
6. Ising machines and combinatorial optimization problem
7. Fully
coupled Ising machines (A case study)
8. Semiconductor Memory and Computing
9. Main Semiconductor Memory Features
10. AI computing with semiconductor
memory (In-Memory Computing)
11. Future Prospects
Takayuki Kawahara is a professor at Tokyo University of Science. He earned his Bachelors, Masters and Doctorate from Kyushu University in 1983, 1985, and 1993, respectively. He has significant experience within both industry and academia and is a member of the IEICE and a fellow of the IEEE.