: Khaled Salah Mohamed
: Neuromorphic Computing and Beyond Parallel, Approximation, Near Memory, and Quantum
: Springer-Verlag
: 9783030372248
: 1
: CHF 62.90
:
: Elektronik, Elektrotechnik, Nachrichtentechnik
: English
: 241
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF

This book discusses and compares several new trends that can be used to overcome Moore's law limitations, including Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing.  The author shows how these paradigms are used to enhance computing capability as developers face the practical and physical limitations of scaling, while the demand for computing power keeps increasing.  The discussion includes a state-of-the-art overview and the essential details of each of these paradigms. 



Khaled Salah Mohamed attended the school of engineering, Department of Electronics and Communications at Ain-Shams University from 1998 to 2003, where he received his B.Sc. degree in Electronics and Communications Engineering with distinction and honors. He received his Masters degree in Electronics from Cairo University, Egypt in 2008. He received his PhD degree in 2012. Dr. Khaled Salah is currently a Technical Lead at the Emulation division at Mentor Graphic, Egypt. Dr. Khaled Salah has published a large number of papers in in the top refereed journals and conferences. His research interests are in 3D integration, IP Modeling, and SoC design.
Preface6
Contents8
Chapter 1: An Introduction: New Trends in Computing14
1.1 Introduction14
1.1.1 Power Wall15
1.1.2 Frequency Wall16
1.1.3 Memory Wall16
1.2 Classical Computing16
1.2.1 Classical Computing Generations17
1.2.2 Types of Computers18
1.3 Computers Architectures19
1.3.1 Instruction Set Architecture (ISA)19
1.3.2 Different Computer Architecture21
1.3.2.1 Von-Neumann Architecture: General-Purpose Processors21
1.3.2.2 Harvard Architecture23
1.3.2.3 Modified Harvard Architecture23
1.3.2.4 Superscalar Architecture: Parallel Architecture23
1.3.2.5 VLIW Architecture: Parallel Architecture24
1.4 New Trends in Computing25
1.5 Conclusions26
References26
Chapter 2: Numerical Computing27
2.1 Introduction27
2.2 Numerical Analysis for Electronics28
2.2.1 Why EDA28
2.2.2 Applications of Numerical Analysis30
2.2.3 Approximation Theory31
2.3 Different Methods for Solving PDEs and ODEs32
2.3.1 Iterative Methods for Solving PDEs and ODEs34
2.3.1.1 Finite Difference Method (Discretization)34
2.3.1.2 Finite Element Method (Discretization)34
2.3.1.3 Legendre Polynomials35
2.3.2 Hybrid Methods for Solving PDEs and ODEs36
2.3.3 ML-Based Methods for Solving ODEs and PDEs36
2.3.4 How to Choose a Method for Solving PDEs and ODEs37
2.4 Different Methods for Solving SNLEs38
2.4.1 Iterative Methods for Solving SNLEs39
2.4.1.1 Newton Method and Newton–Raphson Method39
2.4.1.2 Quasi-Newton Method aka Broyden’s Method42
2.4.1.3 The Secant Method45
2.4.1.4 The Muller Method46
2.4.2 Hybrid Methods for Solving SNLEs47
2.4.3 ML-Based Methods for Solving SNLEs47
2.4.4 How to Choose a Method for Solving Nonlinear Equations47
2.5 Different Methods for Solving SLEs48
2.5.1 Direct Methods for Solving SLEs49
2.5.1.1 Cramer’s Rule Method49
2.5.1.2 Gau