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Introduction

With the rapid increase in the volume of data being generated by various devices, there arises a need for efficient and low-power computing solutions at the edge of the network. Edge computing has emerged as a promising paradigm that enables data processing and analysis to be done closer to the source, reducing latency and bandwidth requirements. Analog-to-digital converters (ADCs) play a critical role in converting real-world signals into digital data for processing and analysis. Traditional ADCs face challenges in terms of power consumption, accuracy, and speed, making them less suitable for edge computing applications.

Memristors, a novel type of passive electronic component, have shown great potential in revolutionizing the design of ADCs. Memristors possess unique properties such as non-volatility, low power consumption, and reconfigurability, making them suitable for implementing energy-efficient and high-performance ADCs for edge computing. This thesis aims to explore the design, implementation, and evaluation of Memristor-based analog-to-digital converters for edge computing applications.

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 Evolution of ADCs
2.2 Memristor technology
2.3 Memristor-based ADCs
2.4 Edge computing
2.5 Challenges in ADC design for edge computing
2.6 Previous research on Memristor-based ADCs
2.7 Comparison of Memristor-based ADCs with traditional ADCs
2.8 Applications of Memristor-based ADCs
2.9 Future trends in ADC design for edge computing
2.10 Conclusion

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Memristor selection and characterization
3.3 ADC design considerations
3.4 Simulation and modeling
3.5 Simulation tools
3.6 Validation methods
3.7 Performance metrics
3.8 Data processing techniques
3.9 Comparison with traditional ADCs

Chapter 4: System Implementation
4.1 Hardware components
4.2 Circuit design
4.3 Layout design
4.4 Fabrication process
4.5 Testing and evaluation
4.6 Power consumption analysis
4.7 Performance optimization techniques
4.8 System integration
4.9 Real-world application scenarios

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Future directions
5.4 Conclusion

Thesis Overview: Memristor-based analog-to-digital converters for edge computing

Edge computing has gained significant attention in recent years as a promising paradigm for enabling efficient data processing and analysis closer to the source. Analog-to-digital converters (ADCs) are essential components in edge computing systems, serving to convert real-world signals into digital data for further processing. Traditional ADCs face challenges in terms of power consumption, accuracy, and speed, limiting their suitability for edge computing applications.

In this thesis, we focus on exploring the design, implementation, and evaluation of Memristor-based ADCs for edge computing. Memristors offer unique properties such as non-volatility, low power consumption, and reconfigurability, making them well-suited for implementing energy-efficient and high-performance ADCs. Through a comprehensive literature review, system design, methodology, and implementation, we aim to demonstrate the potential of Memristor-based ADCs in edge computing applications.

The thesis is structured into five main chapters, starting with an introduction that provides background information, problem statement, objectives, limitations, scope, significance of the study, and the structure of the thesis. Chapter two presents a literature review on the evolution of ADCs, Memristor technology, Memristor-based ADCs, edge computing, challenges in ADC design for edge computing, and previous research on Memristor-based ADCs.

Chapter three focuses on system design and methodology, covering aspects such as system architecture, Memristor selection and characterization, ADC design considerations, simulation and modeling, validation methods, performance metrics, and comparison with traditional ADCs. Chapter four delves into system implementation, detailing hardware components, circuit design, layout design, fabrication process, testing and evaluation, power consumption analysis, performance optimization techniques, and real-world application scenarios.

Finally, chapter five presents the conclusion and summary of the key findings, contributions to the field, future directions, and overall conclusion of the study. This thesis aims to contribute to the advancement of Memristor-based ADCs for edge computing, providing insights into their potential applications and performance advantages over traditional ADCs.

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