05412nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001800172072001700190072002300207072001600230245013500246250001800381264006900399300006800468336002600536337002600562338003600588347002400624505273900648520085203387650002504239650003804264650002504302650003704327650002204364650002404386700008304410700007804493700008804571710003404659773002604693776003604719776003604755776003604791856004604827912001404873912001404887999001704901978-3-031-42478-6DE-He21320251030110920.0cr nn 008mamaa240113s2024    sz |    s    |||| 0|eng d  a97830314247869978-3-031-42478-67 a10.1007/978-3-031-42478-62doi 4aTK7867-7867.5 7aTJFC2bicssc 7aTEC0080102bisacsh 7aTJFC2thema10aDesign and Applications of Emerging Computer Systemsh[electronic resource] /cedited by Weiqiang Liu, Jie Han, Fabrizio Lombardi.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXXIV, 744 p. 397 illus., 293 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aPart-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.  aThis 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, neuromorphic, spintronic, bio-inspired and in-memory computing. Coverage includes the entire stack, i.e., from circuit, architecture, up to system level. This book includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and a range of applications. Serves as a single-source reference to state-of-the-art of emerging computing paradigms; Covers the entire system stack, i.e., from circuit, architecture, up to system level; Includes contributions by leading researchers from academia and industry. 0aElectronic circuits. 0aEmbedded computer systems.964625 0aSolid state physics.14aElectronic Circuits and Systems.24aEmbedded Systems.24aElectronic Devices.1 aLiu, Weiqiang.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9695271 aHan, Jie.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9695281 aLombardi, Fabrizio.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9695292 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303142477908iPrinted edition:z978303142479308iPrinted edition:z978303142480940uhttps://doi.org/10.1007/978-3-031-42478-6  aZDB-2-ENG  aZDB-2-SXE  c57184d57184