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Introduction

The rapid growth of artificial intelligence (AI) applications in recent years has led to an increasing demand for more efficient computing systems. Traditional von Neumann architectures are facing limitations in terms of data transfer speeds and energy efficiency, especially in the field of edge computing where data processing needs to be done closer to where the data is generated. This has led to a growing interest in in-memory computing, where processing and memory are integrated to improve efficiency and reduce latency. Memristors, a type of non-volatile memory device that can also perform logic operations, have emerged as a promising technology for in-memory 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 Memristor technology
2.2 In-memory computing concepts
2.3 Memristor-based in-memory computing
2.4 Edge computing and AI
2.5 Applications of Memristor-based in-memory computing in edge AI
2.6 Challenges in implementing Memristor-based in-memory computing for edge AI
2.7 Previous research in Memristor-based in-memory computing for edge AI
2.8 Comparison with other AI computing architectures
2.9 Future trends in Memristor-based in-memory computing
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 Selection of Memristor devices
3.2 Integration of Memristors with AI algorithms
3.3 Development of edge AI computing system architecture
3.4 Programming framework for Memristor-based in-memory computing
3.5 Data processing and storage techniques
3.6 Testing and evaluation methods
3.7 Performance optimization strategies
3.8 Ethical considerations in edge AI computing
3.9 Data security measures

Chapter 4: System Implementation
4.1 Hardware setup
4.2 Software implementation
4.3 Data collection and preprocessing
4.4 Training AI models on edge devices
4.5 Real-time inference using Memristor-based in-memory computing
4.6 Performance evaluation metrics
4.7 Benchmarking against traditional computing systems
4.8 Integration with existing edge computing platforms

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Recommendations for future research
5.4 Conclusion and final remarks

Thesis Overview:

Memristor-based in-memory computing has been proposed as a potential solution to the limitations of traditional computing architectures in edge AI applications. This thesis aims to explore the feasibility and effectiveness of using Memristors for in-memory computing in edge AI systems. The literature review will provide an overview of Memristor technology, in-memory computing concepts, and previous research in Memristor-based in-memory computing for edge AI. The system design and methodology chapter will focus on the selection of Memristor devices, integration with AI algorithms, and development of an edge AI computing system architecture. The system implementation chapter will detail the hardware and software setup, data processing, and real-time inference using Memristor-based in-memory computing. Finally, the conclusion and summary chapter will provide a summary of findings, contributions to the field, and recommendations for future research.

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