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Introduction

In recent years, the demand for intelligent edge computing devices has been rapidly increasing due to the exponential growth of data generated by various Internet of Things (IoT) applications. Traditional computing systems are reaching their limits in terms of processing power and energy efficiency, leading to the exploration of novel technologies such as neuromorphic computing. Neuromorphic processors, inspired by the human brain, have shown great potential in performing complex cognitive tasks with low power consumption. Additionally, the integration of photonics with neuromorphic processors can further enhance their performance by leveraging the high-speed and energy-efficient nature of light.

This thesis focuses on the design, implementation, and evaluation of photonic neuromorphic processors for edge computing applications. By harnessing the power of light for information processing, these processors have the potential to revolutionize the field of edge computing by enabling real-time and energy-efficient processing of massive amounts of data at the edge of the network. This research aims to address the following key objectives:

1. Investigate the background of photonic neuromorphic processors and their potential applications in edge computing.
2. Identify the challenges and limitations associated with current computing systems for edge applications.
3. Develop a novel system design and methodology for integrating photonics with neuromorphic processors.
4. Implement a photonic neuromorphic processor prototype for edge computing applications.
5. Evaluate the performance of the prototype and compare it with existing edge computing solutions.

This thesis is structured as follows:

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

2. Literature Review
2.1 Overview of Edge Computing
2.2 Neuromorphic Computing
2.3 Photonics in Computing
2.4 Integration of Photonics and Neuromorphic Computing
2.5 Edge Computing Architectures
2.6 Energy-Efficient Computing
2.7 Real-Time Processing
2.8 Edge Computing Applications
2.9 Challenges in Edge Computing
2.10 Existing Edge Computing Solutions

3. System Design and Methodology
3.1 System Architecture
3.2 Photonic Neuromorphic Processor Design
3.3 Optical Computing Techniques
3.4 Neuromorphic Algorithms
3.5 Data Processing and Communication
3.6 Power Optimization Strategies
3.7 Testing and Evaluation
3.8 Performance Metrics
3.9 Simulation Tools
3.10 Validation Methodology

4. System Implementation
4.1 Hardware Requirements
4.2 Software Development
4.3 Integration of Photonics and Electronics
4.4 Testing and Debugging
4.5 Performance Analysis
4.6 Comparison with Existing Solutions
4.7 Scalability and Flexibility
4.8 Security and Privacy Considerations

5. Conclusion
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Concluding Remarks

In summary, this thesis aims to explore the potential of photonic neuromorphic processors for edge computing applications and provide a comprehensive analysis of their performance and capabilities. The integration of photonics with neuromorphic computing has the potential to revolutionize edge computing by enabling real-time and energy-efficient processing of data at the network edge.

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