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Design and Applications of Emerging Computer Systems

This book provides a single-source reference to the state-of-the-art in emerging computer systems. The authors address the technological contributions and developments at various hardware levels of new systems that compute under novel operational paradigms such as stochastic, probabilistic/inexact,...

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Tác giả của công ty: SpringerLink (Online service)
Tác giả khác: Liu, Weiqiang (Biên tập viên), Han, Jie (Biên tập viên), Lombardi, Fabrizio (Biên tập viên)
Định dạng: Điện tử eBook
Ngôn ngữ:Tiếng Anh
Được phát hành: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Phiên bản:1st ed. 2024.
Những chủ đề:
Electronic circuits.
Embedded computer systems.
Solid state physics.
Electronic Circuits and Systems.
Embedded Systems.
Electronic Devices.
Truy cập trực tuyến:https://doi.org/10.1007/978-3-031-42478-6
Các nhãn: Thêm thẻ
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Mục lục:
  • Part-I. In-Memory Computing, Neuromorphic Computing and Machine Learning
  • Chapter 1. Emerging Technologies for Memory-Centric Computing
  • Chapter 2. An overview of Computation-in-Memory (CIM) architectures
  • Chapter 3. Towards Spintronics Non-Volatile Computing-in-Memory Architecture
  • Chapter 4. Is Neuromorphic Computing the Key to Power-Efficient Neural Networks?: A Survey
  • Chapter 5. Emerging Machine Learning using Siamese and Triplet Neural Networks
  • Chapter 6. An active storage system for intelligent data analysis and management
  • Chapter 7. Error-Tolerant Techniques for Classifiers beyond Neural Networks for Dependable Machine Learning
  • Part-II. Stochastic Computing
  • Chapter 8. Efficient Random Number Source Designs Based on D Flip-Flops for Stochastic Computing
  • Chapter 9. Stochastic multipliers from serial to parallel
  • Chapter 10. Ising Models Based On Stochastic Computing
  • Chapter 11. Stochastic and Approximate Computing for Deep Learning: A Survey
  • Chapter 12. Stochastic Computing and Morphological Neural Networks: an ultra-high energy-efficient Machine Learning methodology
  • Chapter 13. Characterizing Stochastic Number Generators for Accurate Stochastic Computing
  • Part-III. Inexact/Approximate Computing
  • Chapter 14. Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic Circuits
  • Chapter 15. Automatic Approximation of Computer Systems through Multi-Objective Optimization
  • Chapter 16. Evaluation of the functional impact of approximate arithmetic circuits on two application examples
  • Chapter 17. Energy Efficient Approximate Floating-Point FFT Design Using A Top-Down Methodology
  • Chapter 18. Approximate Computing in Machine Learning Systems: Cross-level designs and methodologies
  • Chapter 19. Adaptive Approximate Accelerators with Controlled Quality using Machine Learning
  • Chapter 20. Design Wireless Communication Circuits and Systems Using Approximate Computing
  • Chapter 21. Low-cost Logarithmic Floating-point Multipliers for Efficient Neural Network Training
  • Part-IV. Quantum Computing and Other Emerging Computing
  • Chapter 22. Cryogenic CMOS for quantum computing
  • Chapter 23. Memristive Crossbar System towards Hardware Accelaration of Quantum Algorithms
  • Chapter 24. A Review of Posit Arithmetic for Energy Efficient Computation: Methodologies, Applications, and Challenges
  • Chapter 25. Designing Fault Tolerant Digital circuits in Quantum-dot Cellular Automata
  • Chapter 26. CMOS Circuit-based Fully Connected Ising Machines with Parallel Updating and Its Applications in MIMO Detection
  • Chapter 27. Approximate Communication in Network-on Chips for Training and Inference of Image Classification Models.

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