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Introduction:

In recent years, the field of artificial intelligence (AI) has experienced rapid growth and development, leading to significant advances in areas such as computer vision, natural language processing, and autonomous systems. One of the key challenges in AI is the need for efficient hardware platforms that can accelerate the training and inference processes of neural networks. Traditional hardware solutions such as CPUs and GPUs are reaching their limits in terms of computational capability and energy efficiency.

Optical neural networks have emerged as a promising alternative, leveraging the unique properties of light to perform parallel processing and enabling ultra-fast computation speeds. By using light-based components such as waveguides, modulators, and detectors, optical neural networks have the potential to significantly accelerate AI applications while reducing energy consumption.

This thesis explores the design, implementation, and evaluation of optical neural networks for AI acceleration. The research aims to investigate the feasibility and performance of optical neural networks compared to traditional electronic-based systems. By utilizing the principles of optics, this research seeks to address the limitations of current hardware platforms and provide new insights into the potential of optical computing for AI applications.

Table of Contents:

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 Artificial Intelligence
2.2 Traditional Hardware Accelerators for AI
2.3 Optical Computing Fundamentals
2.4 Optical Neural Networks
2.5 Performance Comparison of Optical vs. Electronic Systems
2.6 Recent Advances in Optical Computing
2.7 Challenges and Opportunities in Optical Neural Networks
2.8 Applications of Optical Neural Networks
2.9 Future Directions in Optical Computing
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology

3.1 System Architecture
3.2 Optical Components Selection
3.3 Network Topology Design
3.4 Training Algorithm Implementation
3.5 Inference Algorithm Implementation
3.6 Performance Metrics
3.7 Simulation Setup
3.8 Data Collection Methods
3.9 Evaluation Criteria
3.10 Experimental Design

Chapter 4: System Implementation

4.1 Hardware Setup
4.2 Software Development
4.3 Integration of Optical Components
4.4 Training Data Processing
4.5 Inference Data Processing
4.6 Performance Testing
4.7 Optimization Techniques
4.8 Error Analysis
4.9 Validation Process
4.10 Results Interpretation

Chapter 5: Conclusion

5.1 Summary of Findings
5.2 Discussion of Results
5.3 Contributions to the Field
5.4 Implications for Future Research
5.5 Conclusion and Recommendations

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