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Introduction

Neuromorphic computing and memristive systems have gained significant interest in recent years due to their potential in advancing artificial intelligence (AI) applications, particularly at the edge. As the demand for AI-powered devices continues to grow, there is a need for energy-efficient and high-performance computing systems that can process data in real-time. Neuromorphic computing, inspired by the human brain’s architecture, and memristive systems, which utilize memristors as non-volatile memory and computing elements, offer promising solutions for edge AI 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 Introduction to Neuromorphic Computing
2.2 Memristive Systems and Memristors
2.3 Edge AI Applications
2.4 Neuromorphic Hardware Platforms
2.5 Memristive Crossbar Arrays
2.6 Edge Computing and IoT
2.7 Neuromorphic Algorithms and Models
2.8 Memristive Neuromorphic Systems
2.9 Challenges and Opportunities in Neuromorphic Computing
2.10 Recent Advances in Edge AI Technologies

Chapter 3: System Design and Methodology
3.1 System Architecture Overview
3.2 Neuromorphic Computing Design Principles
3.3 Memristive Systems Integration
3.4 Edge AI Model Development
3.5 Hardware-Software Co-design
3.6 Benchmarking and Performance Evaluation
3.7 Data Preprocessing and Feature Extraction
3.8 Real-time Inference Processing

Chapter 4: System Implementation
4.1 Hardware Platform Selection
4.2 Memristive Device Fabrication
4.3 Software Development and Integration
4.4 System Testing and Validation
4.5 Power Consumption Analysis
4.6 Scalability and Flexibility Assessment
4.7 Security and Privacy Considerations
4.8 Optimization Strategies

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview

In this thesis, we present a comprehensive study on Neuromorphic Computing and Memristive Systems Design for Edge AI applications. We begin by introducing the background of the study, highlighting the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review explores key concepts in neuromorphic computing, memristive systems, edge AI applications, and recent advancements in the field. The system design and methodology chapter detail the design principles, integration of memristive systems, AI model development, and performance evaluation. The system implementation chapter discusses hardware and software aspects, testing, power consumption analysis, and optimization strategies. Finally, the conclusion summarizes the findings, contributions, and outlines future research directions in the field. This thesis aims to contribute to the advancement of energy-efficient and high-performance computing systems for edge AI applications through the use of neuromorphic computing and memristive systems.

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