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Introduction

Neuromorphic computing and memristive systems have gained significant attention in recent years for their potential to revolutionize the field of computing. Neuromorphic computing aims to mimic the brain’s neural networks in hardware, while memristive systems utilize memristors as memory devices with the ability to retain a history of the applied voltage. These technologies offer a promising alternative to traditional computing architectures, with potential applications in artificial intelligence, robotics, and cognitive computing.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of neuromorphic computing
2.2 History of memristive systems
2.3 Neuromorphic hardware implementations
2.4 Memristor technology
2.5 Applications of neuromorphic computing
2.6 Challenges in neuromorphic computing
2.7 Emerging trends in memristive systems
2.8 Comparison of neuromorphic and traditional computing
2.9 Memristive neuromorphic systems
2.10 Future prospects of neuromorphic computing

Chapter 3: System Design and Methodology
3.1 Research design
3.2 Neuromorphic architecture selection
3.3 Memristor selection and integration
3.4 Simulation tools and methodologies
3.5 Data collection and analysis
3.6 Hardware implementation
3.7 Testing and validation
3.8 Performance evaluation

Chapter 4: System Implementation
4.1 Hardware components
4.2 Neuromorphic circuit design
4.3 Memristor fabrication
4.4 Integration of neural networks
4.5 System optimization
4.6 Real-world applications
4.7 Benchmarking and comparison
4.8 Future scalability

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Limitations and future work
5.4 Conclusion and recommendations

Thesis Overview:

Neuromorphic computing and memristive systems have emerged as cutting-edge technologies that have the potential to revolutionize the computing industry. This thesis aims to explore the intersection of these two fields, investigating their synergies and applications in various domains. The introduction provides a comprehensive overview of the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis.

Chapter 2 delves into a detailed literature review, examining the history of neuromorphic computing and memristive systems, their hardware implementations, applications, challenges, emerging trends, and future prospects. Chapter 3 outlines the system design and methodology, including research design, architecture selection, memristor integration, simulation tools, data analysis, hardware implementation, testing, and performance evaluation.

Chapter 4 focuses on the system implementation, discussing hardware components, circuit design, memristor fabrication, neural network integration, optimization, real-world applications, benchmarking, and scalability. Finally, Chapter 5 presents a conclusion and summary of the thesis, highlighting the findings, contributions, limitations, and recommendations for future research in the field of neuromorphic computing and memristive systems.

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