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Neuromorphic Engineering and Neuromorphic Computing

Edited by Prithi Samuel, Rajesh Kumar Dhanaraj, Malathy Sathyamoorthy, Balamurugan Balusamy, and Rutvij H. Jhaver
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394486366  |  Hardcover  |  
861 pages
Price: $225 USD
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One Line Description
Unlock the future of artificial intelligence with this definitive guide to neuromorphic engineering that seamlessly bridges neuroscience, hardware architecture, and machine learning to build brain-inspired, scalable computing systems.

Description
Situated at the intersection of neuroscience, computer science, and engineering, neuromorphic engineering aims to create artificial systems that replicate the parallel processing and learning capabilities of biological neural networks. The interdisciplinary domain of this cutting-edge field draws inspiration from the structure and functionality of the human brain to design and develop advanced computing systems. This two-volume series delves into the fundamental principles and architectural design of neuromorphic computing, emphasizing the creation of fault-tolerant and scalable hardware solutions suitable for large- and medium-scale spiking neural networks equipped with learning capabilities. Furthermore, the volumes provide an exhaustive exploration of the structure and design considerations for a spike-based neuromorphic system, specifically tailored to facilitate communication, computation, and adaptive learning within networks of spiking neurons, all with the aim of addressing the needs of emerging AI applications. Offering a multifaceted exploration of neuromorphic engineering, the series caters to a broad spectrum of readers with diverse backgrounds, enabling them to gain a comprehensive understanding and an enhanced appreciation of this dynamic field.

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Author / Editor Details
Prithi Samuel, PhD is an Assistant Professor in the Department of Computational Intelligence at the SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, with more than 15 years of teaching experience. She has published papers in leading international journals and conferences, as well as several books and chapters. She is a pioneering researcher in the areas of automata theory, machine learning, deep learning, computational intelligence techniques, and the Internet of Things.

Rajesh Kumar Dhanaraj, PhD is a Professor at Symbiosis International University, Pune, India. He has authored and edited more than 50 books on various cutting-edge technologies, holds 21 patents, and has contributed more than 100 articles and papers to esteemed refereed journals and international conferences, in addition to providing chapters for several influential books. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.

Malathy Sathyamoorthy, PhD is an Assistant Professor in the Department of Information Technology at the KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. She has published more than 20 research papers in international journals, 15 papers in international conferences, two patents, and four book chapters. Her research interests include wireless sensor networks, networking, security and machine learning.

Balamurugan Balusamy, PhD is an Associate Dean of Students at Shiv Nadar University with more than 12 years of experience. He has published more than 200 papers in international journals and more than 80 books. His research interests include XAI, data science, and blockchain technologies.

Rutvij H. Jhaver, PhD is an experienced educator and researcher working in the Department of Computer Science and Engineering at Pandit Deendayal Energy University, Gandhinagar, India. He has authored more than 145 articles in international journals and conferences and holds several national and international patents and copyrights. His research interests include cybersecurity, Internet of Things systems, software-defined networking, and smart healthcare.

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Table of Contents
Preface
1. Neuromorphic Computing for Sustainable AI – Evolution, Trends, and Future Challenges
L. Leena Jenifer and K. Indumathi
1.1 Introduction
1.2 History of Neuromorphic Computing
1.2.1 Early Concepts and Theoretical Foundations (1936s–1980s)
1.2.2 Development of Neuromorphic Hardware (1990s–2000s)
1.2.3 Contemporary Neuromorphic Structures (2010s–Present)
1.2.4 Future (2020s): Integration with AI and Sustainable Computing
1.2.5 Technological Progress in Materials and Fabrication
1.2.6 Hybrid Computing Architectures
1.2.7 Focus on Edge Computing
1.2.8 Ethical and Sustainable AI Development
1.3 Innovation
1.3.1 Technology That Saves Energy
1.3.1.1 Spiking NN(SNN)s
1.3.1.2 Conventional Processing
1.3.2 Renewable Substances and Manufacturing
1.3.3 Efficient Production Methods
1.3.4 Instruction-on-Device Learning and Dynamic Processing
1.3.5 Incorporating Sustainable Energy Harvesting Techniques
1.3.6 Cutting Down on Electronic Waste
1.3.6.1 Longer Lifespan
1.3.6.2 Modularity
1.4 Trends
1.4.1 Powerful Efficiency
1.4.2 Spiking Neural Networks (SNNs)
1.4.3 Coordination of Hardware and Algorithm Design
1.4.4 Materials and Fabrication Technologies
1.4.5 Hybrid Computing Architectures
1.4.6 Scalability and Integration
1.4.7 Brain-Inspired Learning Algorithms
1.4.8 Applications in Edge Computing
1.4.9 Interdisciplinarity in Collaboration
1.4.10 Development of AI in an Ethical and Sustainable Way
1.5 Scope of Applications
1.5.1 Recognition of Faces
1.5.2 Applications in Health Services and Biomedicine
1.5.2.1 Peripheral Technology
1.5.2.2 Medical Diagnostics Imaging
1.5.2.3 Agriculture
1.5.2.4 Smart Environment Monitoring System
1.5.2.5 Waste Management and Recycling
1.5.2.6 Security and Surveillance
1.6 Future Challenges
1.6.1 Scalability
1.6.2 Models and Tools for Programming
1.6.3 Dependability and Sturdiness
1.6.4 Optimization of Energy Consumption
1.6.5 Intellectual Proficiency and Biological Stability
1.6.6 Interprofessional Cooperation
1.6.7 Consequences for Society and Ethics
1.6.8 Employment Growth and Pedagogy
1.6.9 Pricing and the Availability
1.6.10 Approval and Comparison
1.7 Conclusion
References
2. Neural Inspired and Neuromorphic Computing Applications:
Framework, Challenges, and Research Directions
Ram Kumar, Monica Sankat and Muskaan Bahri
2.1 Introduction: Unveiling the Potential of Neuromorphic
Computing
2.2 Biological Inspiration for Neuromorphic Computing
2.2.1 Conventional vs. Neuromorphic Computing
2.3 Applications for Neuro-Inspired and Neuromorphic Computing
2.4 Framework for Neuromorphic Computing
2.4.1 Software Tools and Libraries
2.5 Challenges for Neuromorphic Computing
2.5.1 Technical Challenges
2.5.2 Ethical Challenges
2.5.3 Current Efforts to Address Ethical Challenges in Hyper-Realistic Generative AI
2.6 Research Directions for Neuro-Inspired and Neuromorphic Computing
2.7 Conclusion
References
3. Neuromorphic Computing and Green AI Toward a Sustainable AI Revolution
Vinothini A., Aswiga R.V. and Sukanya A.
3.1 Introduction
3.2 Neuromorphic Computing, Green AI, Sustainable AI
3.2.1 Neuromorphic Computing
3.2.2 Green AI
3.2.3 Sustainable AI
3.3 Neuromorphic Computing and Sustainable AI
3.4 Role of Neuromorphic Computing in AI Model Development
3.5 Neuromorphic Software
3.5.1 Spiking Neural Networks
3.5.2 How Does SNN Contribute to Sustainable AI?
3.6 Neuromorphic Hardware for Sustainable AI
3.6.1 Neuromorphic Chips
3.6.2 Neuromorphic Systems from Intel Corporation
3.6.3 Neuromorphic Architectures from Industry and Academia
3.7 AI’s Trail to Sustainability with Neuromorphic Computing and Green AI
3.8 Conclusion
References
4. 2D and 3D Neuromorphic Devices for Sustainable Artificial Intelligence: Applications and Use Cases
Prithi Samuel, Rajesh Kumar Dhanaraj, Malathy Sathyamoorthy, Balamurugan Balusamy, Rutvij H. Jhaveri and Bhavani M.
4.1 Introduction
4.2 2D and 3D Materials in Neuromorphic Technology
4.3 Applications of Neuromorphic Devices
4.3.1 Edge AI
4.3.2 Healthcare: Neuromorphic Devices in Diagnostic and Medical Applications
4.3.3 Robotics: Neuromorphic Technology’s Impact on Adaptive and Intelligent Robotic Systems
4.3.4 Energy Efficiency in Electric Vehicles
4.3.5 Neuromorphic Devices in the Internet of Things (IoT)
4.4 Implementation of Smart Computing Using 2D and 3D Neuromorphic Devices for Sustainable AI Innovations
4.4.1 Design and Fabrication of 2D and 3D Neuromorphic Devices
4.4.2 Integration with AI Algorithms and Spiking Neural Networks (SNNs)
4.4.3 Deployment in Sustainable Applications
4.5 Challenges in Neuromorphic Computing
4.5.1 Scalability and Integration
4.5.2 Complex Fabrication Processes
4.5.3 Material Quality and Uniformity
4.5.4 Energy Efficiency versus Performance Trade-Offs
4.5.5 Compatibility with Existing Technologies
4.6 Conclusion
Bibliography
5. Neuromorphic Computing and Its Industrial Applications:
Tools and Products from Industry Perspective
Yamini J., Jenefa A. and R. Roopa
5.1 Introduction
5.2 Fundamentals of Neuromorphic Computing
5.2.1 Historical Background and Development Milestones
5.2.2 Traditional Computing Architectures
5.2.3 Key Components and Architectures in Neuromorphic Systems
5.3 Neuromorphic Hardware Development
5.3.1 Advances in Neuromorphic Hardware
5.3.2 Case Studies of Industry Leaders
5.3.3 Challenges in Designing and Manufacturing Neuromorphic Hardware
5.4 Applications of Neuromorphic Computing in Industry
5.4.1 Aerospace and Defense
5.4.2 Healthcare
5.4.3 Manufacturing
5.4.4 Finance
5.5 Software and Algorithms
5.5.1 Development of Algorithms
5.5.2 Optimization Techniques Challenges
5.5.3 Integration with Existing Software Infrastructures and Frameworks
5.5.4 Case Studies of Successful Implementations and Industry Impact
5.6 Industrial Use Cases and Case Studies
5.6.1 Aerospace and Defense
5.6.2 Healthcare
5.6.3 Manufacturing
5.6.4 Finance
5.6.5 Quantitative and Qualitative Benefits
5.7 Future Trends and Emerging Technologies
5.7.1 Future of Neuromorphic Computing in Industry
5.7.2 Research Directions and Technologies
5.7.3 Impact of Neuromorphic Computing in Global Industries and Economies
5.8 Conclusion
References
6. Sustainable AI and Deep Learning Capabilities with Neuromorphic Computing for Healthcare
Bharathi Chakrapani, G. Saranya, M. Safa, K. Meenakshi, Kannan Chakrapani (K. Chakrapani) and M. A. Al-Khasawneh
6.1 Introduction to Sustainable AI in Healthcare
6.1.1 Overview of Sustainability Challenges in AI-Driven Healthcare Systems
6.1.2 Need for Energy-Efficient Solutions in the Era of Advanced Computing
6.2 Deep Learning in Healthcare: Transformative Potential and Challenges
6.2.1 Role of Deep Learning in Diagnostics, Treatment Planning, and Personalized Medicine
6.2.1.1 Diagnostics
6.2.1.2 Treatment Planning
6.2.1.3 Personalized Medicine
6.2.2 Computational and Energy Demands of Deep Learning Models in Healthcare Applications
6.2.2.1 High Computational Requirements
6.2.2.2 Energy Consumption
6.2.2.3 Trade-Offs in Model Complexity
6.2.3 Challenges in Deep Learning for Healthcare
6.3 Neuromorphic Computing: Fundamentals and Advantages
6.3.1 Biological Inspiration Behind Neuromorphic Architectures
6.3.2 Key Features of Neuromorphic Computing
6.3.3 Advantages of Neuromorphic Computing
6.4 Applications of Neuromorphic AI in Healthcare
6.4.1 Real-Time Disease Diagnostics and Prediction
6.4.2 Intelligent Wearable Devices for Continuous Monitoring
6.4.3 Edge Computing in Telemedicine and Remote Healthcare
6.5 Applications of Neuromorphic AI in Healthcare
6.5.1 Real-Time Disease Diagnostics and Prediction
6.5.2 Intelligent Wearable Devices for Continuous Monitoring
6.5.3 Edge Computing in Telemedicine and Remote Healthcare
6.6 Ethical Considerations and Challenges in Sustainable Healthcare AI
6.6.1 Balancing Efficiency with Data Privacy and Security
6.6.2 Addressing Biases in AI Systems and Ensuring Equitable Access
6.7 Conclusion
References
7. Neuromorphic Computing Algorithms for Collaborative
Autonomous Systems: Opportunities and Future Revolution
Mary Subaja Christo, P. Visalakshi, S. Thanga Revathi, V. Elizabeth Jesi and Firoz Khan
7.1 Introduction to Neuromorphic Computing
7.1.1 Neuromorphic Computing Concepts
7.1.2 Key Distinctions from Traditional and AI-Based Computing
7.1.3 Significance of Neuromorphic Computing
7.2 Neuromorphic Architectures and Algorithms
7.2.1 Spiking Neural Networks in Neuromorphic Systems
7.2.2 Algorithmic Innovations for Real-Time Processing
7.2.3 Applications in Collaborative Autonomous Systems
7.3 Collaborative Autonomous Systems: Definition and Applications
7.3.1 Characteristics and Requirements of Collaborative Autonomous Systems
7.3.2 Current Applications and Limitations Addressed by Neuromorphic Computing
7.4 Neuromorphic Hardware for Autonomous Systems
7.4.1 Loihi - Intel’s Neuromorphic Chip
7.4.2 SpiNNaker - The Spiking Neural Network Architecture
7.4.3 TrueNorth - IBM’s Neuromorphic Chip
7.4.4 Performance Analysis: Energy Efficiency and Scalability
7.4.4.1 Energy Efficiency
7.4.4.2 Scalability
7.5 Edge Computing and Decentralization with Neuromorphic Systems
7.5.1 Integrating Neuromorphic Computing for Edge Intelligence
7.5.2 Benefits in Reducing Latency
7.5.3 Improving Energy Efficiency
7.5.4 Enabling Decentralization
7.6 Role of Neuromorphic Algorithms in Swarm Intelligence
7.7 Conclusion
References
8. Towards Sustainable Electronics Building Brain-Inspired
Computer Chips for Next-Generation AI
Vijay K., Eugene Berna I., Ravikumar S., Jeyalakshmi J. and Prithi Samuel
8.1 Introduction
8.1.1 The Growing Need for Sustainable Electronics
8.1.2 Challenges in Traditional AI Computing Architectures
8.1.3 The Vision of Brain-Inspired Computing
8.2 Understanding Neuromorphic Computing
8.2.1 What is Neuromorphic Computing?
8.2.2 Key Principles of Brain-Inspired Chips
8.2.3 Comparison with Traditional and Quantum Computing
8.3 Designing Brain-Inspired Computer Chips
8.3.1 Materials and Architectures for Neuromorphic Systems
8.3.2 The Role of Synaptic Plasticity and Neural Networks
8.3.3 Energy Efficiency and Scalability in Chip Design
8.4 Applications in Next-Generation AI
8.4.1 Advancing AI in Healthcare, Robotics, and IoT
8.4.2 Real-World Case Studies of Neuromorphic Systems
8.4.3 Performance Benchmarks and Metrics
8.5 Challenges and Future Directions
8.5.1 Overcoming Technical and Scalability Barriers
8.5.2 Ethical Considerations and Environmental Impacts
8.5.3 The Roadmap to Widespread Adoption
8.6 Conclusion
8.6.1 Recap of Key Insights
8.6.2 The Promise of Sustainable Electronics in AI Evolution
8.6.3 Final Thoughts on the Future of Brain-Inspired Technologies
References
9. Neuromorphic Computing for Intelligent Edge AI: Challenges and Research Perspective
Usharani R., Kanimozhi N., Sumathy G., Abijah Roseline S.,
Angelin Claret S. P., Sherin Shibi C. and Alice Nithya A.
9.1 Introduction
9.2 Potential of Neuromorphic Computing
9.3 Challenges Hindering Widespread Adoption
9.3.1 Hardware Limitations
9.3.1.1 Maturity and Performance Gaps
9.3.2 Algorithmic Inefficiency
9.3.2.1 Leveraging Neuromorphic Strengths
9.3.3 Development Tool Immaturity
9.3.3.1 Streamlining Design and Deployment
9.4 Research Efforts Addressing the Challenges
9.4.1 Scalability Considerations for Neuromorphic Smart Edge AI
9.4.1.1 Miniaturization
9.4.1.2 Integration with Existing Frameworks
9.4.1.3 Development of Scalable Neuromorphic Devices
9.4.2 Defining Key Performance Indicators for Neuromorphic Smart Edge AI
9.4.3 Energy Optimization in Smart Edge AI Applications
9.4.4 Enhancing the Robustness and Reliability of Neuromorphic Implementations
9.5 Research Perspective
9.5.1 Closing the Gap between Hardware and Algorithms
9.5.1.1 Co-Design of Hardware and Algorithms
9.5.1.2 Standardization and Interoperability
9.5.1.3 Enhanced Simulation and Modeling Tools
9.5.1.4 Feedback Mechanisms for Constant Enhancements
9.5.1.5 Cross-Disciplinary Collaboration
9.5.2 Enabling a New Era of Intelligent Edge Devices
9.5.2.1 Energy Efficiency and Sustainability
9.5.2.2 Real-Time Processing and Low Latency
9.5.2.3 Scalability and Integration
9.5.2.4 Robustness and Reliability
9.5.2.5 Advanced Learning and Adaptation
9.5.2.6 Security and Privacy
9.5.2.7 Human-Machine Interaction
9.6 Conclusion
References
10. Neuromorphic Computation for Sustainable AI in Aerospace and Defense Sectors: Trends and Opportunities
P. Sivaprakash, T. N. Prabhu, S. Karthika, Liza Gernal, K. Juliana Gnanaselvi and T. Sivaprakasam
10.1 Introduction
10.2 Neuromorphic Computing
10.3 Applications of Neuromorphic Computing in Aerospace and Defense
10.3.1 Autonomous Systems
10.3.1.1 The Role of Neuromorphic Computing in Autonomous Systems
10.3.1.2 Enhancing Real-Time Decision Making
10.3.1.3 Processing Sensory Data
10.3.1.4 Learning and Adaptation
10.3.1.5 Practical Applications in Aerospace
10.3.2 Sensor Data Processing
10.3.3 Cognitive Radar Systems
10.4 Sustainability Benefits of Neuromorphic Computing
10.5 Challenges and Limitations
10.5.1 Hardware Development
10.5.1.1 Challenges in Neuromorphic Hardware Development
10.5.1.2 Technological Innovations in Neuromorphic Hardware
10.5.1.3 Future Directions in Neuromorphic Hardware Development
10.5.2 Algorithmic Development
10.5.3 Ethical and Security Considerations
10.6 Future Trends and Opportunities
10.6.1 Neuromorphic Chips and Hardware
10.6.2 AI-Driven Innovation
10.6.3 Policy and Regulation
10.6.3.1 Ethical Frameworks for Neuromorphic Computing
10.6.3.2 International Cooperation and Standardization
10.7 Conclusion
Bibliography
11. Neuromorphic Computing for Autonomous Vehicles and Autonomous Racing
A. Pandian, C. Vijayakumaran, M.S. Abirami, M. Ferni Ukrit and S. Padmini
11.1 Introduction to Neuromorphic Computing
11.1.1 Key Principles of Neuromorphic Computing
11.1.2 Comparison with Conventional AI and Deep Learning
11.2 Neuromorphic Hardware for Autonomous Systems
11.2.1 Spiking Neural Networks (SNNs)
11.2.2 Neuromorphic Processors (Loihi, TrueNorth, BrainChip Akida)
11.3 Neuromorphic Perception and Sensor Fusion
11.3.1 Event-Based Vision and LiDAR Processing
11.3.2 Edge Computing and Real-Time Decision-Making
11.4 Neuromorphic Control for Autonomous Vehicles
11.4.1 Adaptive Path Planning
11.4.2 Efficient Navigation Algorithms
11.5 Neuromorphic Computing in Autonomous Racing
11.5.1 Quick Decision-Making
11.5.2 Reinforcement Learning and Neuromorphic Adaptation
11.6 Challenges and Limitations of Neuromorphic Computing
11.6.1 Hardware Limitations
11.6.2 Training and Scalability Problems
11.7 Future Directions and Emerging Trends
11.7.1 Brain-Inspired AI for Driverless Cars
11.7.2 Potential Applications Beyond Transportation
11.8 Conclusion
References
12. Neuromorphic Computing and the Future of Sustainable AI
in Smart Manufacturing
Murugaanandam S., Karthikeyan H., Angayarkanni S. A. and Balakiruthiga B.
12.1 Introduction to Neuromorphic Computing and AI in Manufacturing
12.1.1 Evolution of AI in Industrial Automation
12.1.2 Fundamentals of Neuromorphic Computing: Brain‑Inspired Processing
12.1.3 Role of AI and Neuromorphic Systems in Industry 4.0
12.2 Energy Efficiency and Sustainability in AI Systems
12.2.1 The Standard AI Models Consume Excessive Amounts of Power
12.2.2 The New Computing Method Helps Cut Down on Energy Use
12.2.3 Goals of Environmental Impact and Sustainability in Smart Factories
12.3 Neuromorphic Processors: Architectures and Capabilities
12.3.1 Overview of Leading Neuromorphic Chips (IBM TrueNorth, Intel Loihi)
12.3.2 Spike-Based Neural Networks and Event-Driven Computation
12.3.3 Parallelism and Real-Time Processing in Neuromorphic Systems
12.4 Applications of Neuromorphic Computing in Smart Manufacturing
12.4.1 Predictive Maintenance and Fault Detection
12.4.2 Smart Quality Control and Defect Detection
12.4.3 Autonomous Robotic Systems and Real-Time Process Optimization
12.5 Hybrid AI Models: Combining Neuromorphic and Traditional AI
12.5.1 Integrating Deep Learning with Neuromorphic Computing
12.5.2 Advantages of Hybrid AI in Decision-Making
12.5.3 Hybrid AI Case Studies in Smart Manufacturing
12.6 Challenges and Implementation Barriers in Industry 4.0
12.6.1 Hardware and Software Integration Issues
12.6.2 The Training Process of Neuromorphic Models for Industrial Usage
12.6.3 Cost and Adoption Challenges for Manufacturers
12.7 Future Trends: Digital Twins, IoT, and Edge AI
12.7.1 Role of Neuromorphic Computing in Digital Twin Technology
12.7.2 Edge AI Boosts the Effectiveness of IoT-Driven Smart Factories by Enhancing Their Operations
12.7.3 Next-Generation AI Models for Real-Time Manufacturing Insights
12.8 Ethical and Economic Considerations in Sustainable AI Adoption
12.8.1 Balancing Efficiency with Workforce Implications
12.8.2 Economic Feasibility and Return on Investment in Neuromorphic AI
12.8.3 Regulatory and Ethical Concerns in AI-Driven Manufacturing
12.9 Conclusion
References
13. Neuromorphic Computing and Sustainable AI Innovation in the Banking, Financial Services, and Insurance Industry (BFSI)
A.K. Reshmy, K. Babu, S. Sabaria and Sivajothi Ramalingam
13.1 Introduction to Neuromorphic Computing and Sustainable AI in BFSI
13.1.1 Overview of Neuromorphic Computing
13.1.2 Importance of Sustainable AI
13.1.3 Relevance to the BFSI Industry
13.1.4 Objectives and Scope of the Study
13.2 Neuromorphic Computing: Principles and Applications in BFSI
13.2.1 Fundamental Concepts of Neuromorphic Computing
13.2.2 Hardware and Software Components
13.2.3 Case Studies in Banking
13.2.4 Applications in Financial Services
13.2.5 Use Cases in the Insurance Industry
13.3 Sustainable AI: Concepts and Implementation in BFSI
13.3.1 Defining Sustainable AI
13.3.2 Environmental Impact of AI Technologies
13.3.3 Strategies for Implementing Sustainable AI
13.3.4 Benefits of Sustainable AI in BFSI
13.3.5 Challenges and Solutions
13.4 Integration of Neuromorphic Computing and Sustainable AI in BFSI Operations
13.4.1 Enhancing Data Processing and Analysis
13.4.2 Improving Risk Management and Fraud Detection
13.4.3 Personalizing Customer Experience
13.4.4 Streamlining Regulatory Compliance
13.4.5 Operational Efficiency and Cost Reduction
13.5 Future Trends and Conclusion
13.5.1 Emerging Trends in Neuromorphic Computing
13.5.2 Future of Sustainable AI in BFSI
13.5.3 Potential Impact on the Industry
13.5.4 Summary of Key Findings
13.5.5 Recommendations for BFSI Stakeholders
13.5.6 Concluding Remarks
References
14. Case Studies and Real-World Implementations of Neuromorphic Computing for Sustainable AI
M. Saranya and A.S. Muthanantha Murugavel
14.1 Introduction
14.2 Principles of Neuromorphic Computing
14.2.1 Neurons and Synapses
14.2.2 Brain Architecture
14.2.3 Hardware Architecture
14.2.3.1 Neuromorphic Chips
14.2.3.2 Energy Efficiency
14.2.3.3 Scalability
14.2.4 Software Design
14.2.4.1 Neuromorphic Algorithms
14.2.4.2 Programming Paradigms
14.2.4.3 Simulation and Testing
14.2.4.4 Key Components and Technologies
14.2.5 Advantages of Neuromorphic Computing
14.2.6 Applications in Sustainable AI
14.3 Case Studies in Sustainable AI
14.3.1 Case Study 1: IBM’s TrueNorth
14.3.1.1 Overview
14.3.1.2 Implementation
14.3.1.3 Impact
14.3.1.4 Objectives
14.3.1.5 Technological Approach
14.3.1.6 Key Innovations
14.3.1.7 Key Features
14.3.1.8 Applications
14.3.1.9 Example: DARPA SyNAPSE Program
14.3.1.10 Challenges and Limitations
14.3.1.11 Future Directions
14.3.2 Case Study 2: Intel’s Loihi
14.3.2.1 Overview
14.3.2.2 Key Features
14.3.2.3 Applications
14.3.2.4 Implementation
14.3.2.5 Impact
14.3.2.6 Example: NASA FDL Project
14.3.2.7 Challenges and Limitations
14.3.2.8 Future Directions
14.3.3 Case Study 3: BrainScaleS
14.3.3.1 Overview
14.3.3.2 Key Features
14.3.3.3 Applications
14.3.3.4 Implementation
14.3.3.5 Impact
14.3.3.6 Example: Neurorobotics Experiments
14.3.3.7 Challenges and Limitations
14.3.3.8 Future Directions
14.3.4 Case Study 4: Spiking Neural Networks in Healthcare
14.3.4.1 Overview
14.3.4.2 Implementation
14.3.4.3 Impact
14.3.4.4 Example: Seizure Prediction and Detection
14.3.4.5 Challenges and Limitations
14.3.4.6 Future Directions
14.3.5 Case Study 5: Neuromorphic Computing in Environmental Monitoring
14.3.5.1 Overview
14.3.5.2 Implementation
14.3.5.3 Impact
14.3.5.4 Example: Smart Cities Initiative
14.3.5.5 Challenges and Limitations
14.3.5.6 Future Directions
14.3.5.7 Real-World Implementations
14.4 Implementation
14.4.1 Implementation 1: IBM’s TrueNorth
14.4.1.1 Overview
14.4.1.2 Applications
14.4.1.3 Sustainability Impact
14.4.1.4 Case Study
14.4.2 Implementation 2: Intel’s Loihi
14.4.2.1 Overview
14.4.2.2 Applications
14.4.2.3 Sustainability Impact
14.4.2.4 Case Study
14.4.3 Implementation 3: BrainScaleS
14.4.3.1 Overview
14.4.3.2 Applications
14.4.3.3 Sustainability Impact
14.4.3.4 Case Study
14.4.4 Implementation 4: Spiking Neural Networks in Healthcare
14.4.4.1 Overview
14.4.4.2 Applications
14.4.4.3 Sustainability Impact
14.4.4.4 Case Study
14.4.5 Implementation 5: Neuromorphic Computing in Environmental Monitoring
14.4.5.1 Overview
14.4.5.2 Applications
14.4.5.3 Sustainability Impact
14.4.5.4 Case Study
14.4.6 Implementation 6: Brain-Inspired Computing for Smart Cities
14.4.6.1 Overview
14.4.6.2 Applications
14.4.6.3 Sustainability Impact
14.4.6.4 Case Study
14.4.7 Implementation 7: Autonomous Systems
14.4.7.1 Overview
14.4.7.2 Applications
14.4.7.3 Sustainability Impact
14.4.7.4 Case Study
14.4.8 Implementation 8: Industrial Automation
14.4.8.1 Overview
14.4.8.2 Applications
14.4.8.3 Sustainability Impact
14.4.8.4 Case Study
14.4.9 Implementation 9: Agricultural Technology
14.4.9.1 Overview
14.4.9.2 Applications
14.4.9.3 Sustainability Impact
14.4.9.4 Case Study
14.4.10 Implementation 10: Neuromorphic Computing in Space Exploration
14.4.10.1 Overview
14.4.10.2 Applications
14.4.10.3 Sustainability Impact
14.4.10.4 Case Study
14.5 Challenges in Neuromorphic Computing
14.5.1 Hardware Limitations
14.5.1.1 Overview
14.5.1.2 Key Issues
14.5.1.3 Future Directions
14.5.2 Software and Algorithm Development
14.5.2.1 Overview
14.5.2.2 Key Issues
14.5.2.3 Future Directions
14.5.3 Energy Efficiency and Power Management
14.5.3.1 Overview
14.5.3.2 Key Issues
14.5.3.3 Future Directions
14.5.4 Integration with Existing Systems
14.5.4.1 Overview
14.5.4.2 Key Issues
14.5.4.3 Future Directions
14.5.5 Cost and Accessibility
14.5.5.1 Overview
14.5.5.2 Key Issues
14.5.5.3 Future Directions
14.6 Future Directions in Neuromorphic Computing
14.6.1 Advancements in Neuromorphic Hardware
14.6.1.1 Overview
14.6.1.2 Key Areas
14.6.2 Development of Neuromorphic Software Ecosystems
14.6.2.1 Overview
14.6.2.2 Key Areas
14.6.3 Application-Specific Neuromorphic Systems
14.6.3.1 Overview
14.6.3.2 Key Areas
14.6.4 Interdisciplinary Research and Collaboration
14.6.4.1 Overview
14.6.4.2 Key Areas
14.6.5 Ethical and Societal Considerations
14.6.5.1 Overview
14.6.5.2 Key Areas
14.7 Synthesis of Key Insights
Bibliography
15. Enhancing Human Performance: Integrating Neuromorphic
Physiological Signal Processing Systems for Monitoring
Vanila Sildas, Rathinam Ananthanarayanan, Elango Kannan and Beaulah Jeyavathana R.
15.1 Introduction
15.1.1 Input Signals
15.1.2 Signal Processing Module
15.1.3 Module of Performance Assessment
15.1.4 Feedback and Intervention
15.1.5 Data Storage and Analysis
15.1.6 User Interface
15.1.7 Integration with Other External Systems
15.2 System Architecture
15.2.1 Input Signals
15.2.2 Signal Processing Module
15.2.3 Performance Assessment Module
15.2.4 Feedback and Intervention
15.2.4.1 Activity Adjustments
15.2.4.2 Training Modifications
15.2.4.3 Stress Management
15.2.5 Data Storage and Analysis
15.2.5.1 Data Storage
15.2.5.2 Long-Term Analysis
15.2.5.3 Data Sharing
15.2.6 User Interface
15.2.6.1 Visualization
15.2.6.2 Feedback Delivery
15.2.6.3 User Input
15.2.7 Integration with External Systems
15.2.7.1 Wearable Sensors
15.2.7.2 Rehabilitation Equipment
15.2.7.3 Computer Interfaces
15.3 Implementation
15.4 Results and Discussion
15.4.1 Physiological Signal Monitoring
15.4.2 Signal Preprocessing and Feature Extraction
15.4.3 Stress and Workload Assessment
15.4.4 Performance Metrics and Real-Time Feedback
15.4.5 Data Storage and Long-Term Tracking
15.4.6 Integration with External Systems
15.5 Conclusion
References
Preface
16. Categorical Inquiry on Advancement of Neuromorphic Engineering: From Obstacles to Opportunities
A. Rehash Rushmi Pavitra, A.M Kalpana and D. Rajalakshmi
16.1 Introduction into Von Neumann Architecture
16.2 What Makes Neuromorphic Systems Important?
16.3 The Transformation to Neuromorphic Computing
16.4 Architectures for Neuromorphic Computing
16.5 Types of Neuromorphic Computing
16.6 Hardware and Architectures for Neuromorphic Systems
16.7 Neural Processing Hierarchical System
16.8 Applications of Neuromorphic Computing
16.9 Obstacles in Neuromorphic Computation
16.10 Spiking Neural Networks (SNNs)
16.11 Neural Computing
16.12 Conclusion
References
17. Neuromorphic Engineering Versus Neuromorphic Computing: Pros and Cons
Saranya S. S., Kavitha M. N., Santhosh C., Giriprasad K. S. and Firoz Khan
17.1 Introduction to Neuromorphic Engineering
17.2 Materials and Devices Used in Neuromorphic Engineering
17.2.1 Memristors
17.2.2 Phase Change Memory (PCM)
17.2.3 Ferroelectric Transistors
17.2.4 Nanowire Networks
17.2.5 Brain-Machine Interface (BMI) Devices
17.2.5.1 Invasive BMIs
17.2.5.2 Non-Invasive BMIs
17.2.5.3 Semi-Invasive BMIs
17.3 Neuromorphic Computing
17.3.1 Models of Neuromorphic Computing
17.3.1.1 Neuron Model
17.3.1.2 Synapse Models
17.3.2 Neuromorphic Algorithms
17.3.2.1 Training or Learning of Spiking Neural Networks Algorithms (SNNs)
17.3.2.2 Non-Machine Learning Algorithms
17.3.3 Applications of Neuromorphic Computing
17.4 Neuromorphic Computing vs. Neuromorphic Engineering
17.5 Conclusion
Bibliography
18. Human–Computer Interaction and Neuromorphic Systems:
Hardware and Software Components and Functionalities
C.N. Vanitha, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Mahmoud Ahmad Al-Khasawneh
18.1 Introduction
18.1.1 History
18.1.2 Neuromorphic Hardware and Software
18.1.3 Neuromorphic Thematic Areas
18.2 Neuromorphic Computing
18.2.1 Need for Enhanced Computing
18.2.2 Implementation Needs
18.2.2.1 Neuromorphic Concepts
18.2.2.2 Building Blocks
18.2.3 Properties of Neuromorphic Engineering
18.3 Opportunities for Neuromorphic Computing
18.3.1 Neurons
18.3.2 Synapses
18.4 von Neumann Architecture vs. Neuromorphic Architecture
18.4.1 System Level
18.4.2 Device Level
18.4.3 Performance Level
18.5 Neurological Inspiration
18.5.1 Biological Computation Process
18.6 Neuromorphic Computing vs. AI
18.7 Potential Advantages of Neuromorphic Computing
18.7.1 Potential Drawbacks in Neuromorphic Computing
18.8 State of the Technology
18.8.1 Top Research Institutions
18.8.2 Social Issues Management
18.9 Additional Consideration for Neuromorphic Computing
18.10 Advantages of Neuromorphic Computing
18.10.1 Disadvantages of Neuromorphic Computing
18.11 Hardware Components
18.11.1 Software Components
18.12 HCI and Neuromorphic Systems Crossover
18.13 What is HCI?
18.13.1 HCI Meteoric Rise
18.13.2 Vast Neuromorphic Networks
18.14 Conclusion
Bibliography
19. Integration of Neuromorphic Computing Techniques in Edge Devices: Statistical Analysis of Energy Consumption Strategies
Akshya J., S. Dhamodaran, Aruna M., Arthi B. and Sundarrajan M.
19.1 Introduction
19.1.1 Motivation for Edge Intelligence
19.1.2 Neuromorphic Computing Role
19.2 Neuromorphic Computing
19.2.1 Spiking Neural Networks and Biological Inspiration
19.2.2 Hardware Platforms and Neuromorphic Architectures
19.2.3 Conventional Computing Overview
19.3 Edge Devices
19.3.1 Edge Computing Environments Characteristics
19.3.2 Performance and Power Trade-Offs in Edge Devices
19.3.3 Energy-Efficient Computational Models Needs
19.4 Neuromorphic Computing Integration into Edge Systems
19.4.1 Embedded Neuromorphic System Architecture
19.4.2 Communication and Processing with SNNs
19.4.3 Hardware Limitations and Design Challenges
19.5 Strategies in Neuromorphic Edge Devices
19.5.1 Computation and Sparse Activity
19.5.2 Frequency Scaling and Dynamic Voltage
19.5.3 Data Movement Optimization and Memory Access
19.6 Static Energy Consumption Analysis
19.6.1 Data Collection
19.6.2 Regression Model
19.7 Comparative Evaluation
19.7.1 Latency and Energy Comparisons
19.7.2 Model Accuracy vs. Power Consumption
19.8 Challenges, Limitations, and Future Perspectives
19.8.1 Neuromorphic Hardware Scalability at the Edge
19.8.2 Emerging Standards and Protocols Integration
19.9 Conclusion
References
20. Evolution of Memory Based Hardware Devices: An Impact
on Neuromorphic Engineering
M. Mohankumar, Roshini Begam M., Sanjana S., Prithika G.L., Vaishnavi S. and Sudeshna M.
20.1 Introduction
20.2 Resistive Random-Access Memory (ReRAM)
20.3 Multisensory Memristor
20.4 Spintronics Setup
20.5 Transistors Synaptic
20.6 Discussion and Conclusion
Bibliography
21. Networks, Standards, and Communication Protocols for Neuromorphic Systems
M. Pradeepa, B. Prabadevi, S. Kumaraperumal and S. Sudhagara Rajan
21.1 Introduction
21.2 Neuromorphic Network Systems
21.2.1 Neuron Models
21.2.2 Feed-Forward Network
21.2.3 Recurrent Networks
21.3 Neuromorphic Hardware
21.3.1 Analogue/Mixed CMOS Hardware Systems
21.3.1.1 Neurogrid
21.3.1.2 Braindrop
21.3.1.3 BrainscaleS
21.3.1.4 Dynap-SEL
21.3.2 Digital CMOS Hardware Systems
21.3.2.1 Spinnaker
21.3.2.2 TrueNorth
21.3.2.3 Loihi
21.3.2.4 Darwin
21.3.2.5 Tianjic
21.3.2.6 Akida
21.3.2.7 NeuronFlow
21.4 Neuromorphic Communication
21.4.1 Neuromorphic Encoding
21.4.2 Multiplexing
21.4.3 Synchronization
21.5 Neuromorphic Protocols
21.5.1 Data Specification Protocol
21.5.2 The SpiNNaker Datagram Protocol (SDP)
21.5.3 The SpiNNaker Command Protocol (SCP)
21.5.4 External Internal Event Input Output (EIEIO)
21.5.5 Data Specification Execution (DSE)-Online Protocol
21.5.6 Routing Method
21.6 Challenges to Adoption and Integration
21.6.1 Lack of Neuromorphic Experts
21.6.2 Local Communication Challenges Within a Neuromorphic Chip
21.6.3 Global Communication Challenges
21.6.4 Programming Abstractions and Frameworks
21.6.5 Inter-Device Communication
21.6.6 Reliance on Host Computers
21.7 Conclusion
References
22. Neuromorphic System-Based Adaptive Filters and Classifiers: A Machine Learning Perspective
S. Muthu Lakshmi, T. Sangeetha, Prithi Samuel and Balamurugan Balusamy
22.1 Introduction
22.2 Related Works
22.3 Limitations in Neuromorphic System
22.4 Component of the Neuromorphic System
22.4.1 Spiking Neural Network (SNN)
22.4.2 Sketch of Neuromorphic System
22.5 Adaptive Filters in Neuromorphic System
22.5.1 Neural Adaptation Mechanisms
22.5.2 Spike-Timing-Dependent Plasticity (STDP)
22.5.2.1 STDP Key Components
22.5.2.2 Computational Models
22.6 Adaptive Filter Algorithms in Neuromorphic Systems
22.6.1 Least Mean Squares (LMS) Algorithm
22.6.2 Recursive Least Squares (RLS) Algorithm
22.7 Classifiers in Neuromorphic Systems
22.7.1 Liquid State Machines (LSM)
22.7.1.1 Neuron Dynamics
22.7.1.2 Input Layer
22.7.1.3 Reservoir
22.7.1.4 Readout Layer
22.7.2 Convolutional Spiking Neural Networks (CSNN)
22.7.2.1 Input Encoding in CSNN
22.7.2.2 Convolutional Layer
22.7.2.3 Spiking Neuron Model (LIF Model)
22.7.2.4 Pooling Layer
22.7.2.5 Synaptic Input
22.7.2.6 Output Layer
22.7.2.7 Learning Rule
22.8 Conclusion
References
23. Integration of Neuromorphic Systems in Industry 4.0: An Evolution Perspective
Pratik S. Patel, Kiran Kakade and M. Nalini
23.1 Introduction
23.1.1 Survey Objective
23.1.2 Existing Surveys
23.1.3 Establishing the Link Between CNNs and Hardware
23.1.4 Linking Industrial IoT, CNNs, and Hardware Accelerators
23.2 Neuromorphic Computing Principle
23.2.1 Architecture for Smart Agriculture
23.2.1.1 Artificial Synapse
23.2.1.2 Two-Terminal Artificial Synapse
23.2.2 Three-Terminal Artificial Synapse
23.2.3 Artificial Neurons
23.2.4 Artificial Neural Network
23.2.5 Neuromorphic Computing
23.2.5.1 History of Neuromorphic Computing
23.2.5.2 An Outlook of Neuromorphic Computing
23.2.6 Neuromorphic Hardware
23.2.7 The Field of Neuromorphic Networks
References
24. Neuromorphic Engineering Applications in Health 4.0:
Review of Recent Developments and Future Directions
Bhupinder Singh and Christian Kaunert and Saurabh Chandra
24.1 Introduction and Background
24.1.1 Neuromorphic Engineering: Definition and Importance
24.1.1.1 Overview of Neuromorphic Engineering
24.1.1.2 Traditional Computing Paradigms vs. Agoric Systems
24.1.1.3 Relevance in Healthcare
24.1.2 Intersection of Neuromorphic Engineering and Health 4.0: Role of Digital Health, AI, IoT and Big Data in Healthcare
24.2 Neuromorphic Engineering: Technical Background
24.3 Recent Developments in Neuromorphic Engineering for Health 4.0
24.3.1 Applications of Neuromorphic Chips in Healthcare
24.3.2 Real-Time Neuromorphic Monitoring Sensors
24.3.3 Neuroprosthetics and Rehabilitation Using Neuromorphic Systems
24.3.4 Integration of AI and ML in Neuroscience
24.3.5 Neuromorphic Healthcare: The Next Horizon
24.4 Biomedical Neuromorphic Sensing: Real-Time Health Monitoring with Neuromorphic Sensors
24.4.1 Wearable Devices, Prosthetics, and Implants Case Studies
24.4.2 Neuromorphic Processing in Medical Imaging and Diagnostics: Processing Medical Images (MRI, CT Scans, Etc.)
24.4.3 Role in Diagnostics and Disease Detection
24.4.4 Case Study: Neurological Disorders Detection Early
24.4.5 Neuromorphic Robotics in the Healthcare Field: Neuromorphic Chips within Healthcare Robotics
24.4.6 Applications: Examples Include Surgical Robots, Rehabilitation and Assisting Tool
24.4.7 Personalized Neuromorphic Systems for Medicine: Personalized Treatment Plans through AI
24.4.8 Genomics and Pharmacology Involvement
24.4.9 Neuromorphic Applications in Personalized Healthcare
24.5 Benefits of Neuromorphic Engineering in Health 4.0
24.5.1 Monitoring and Early Prediction in Real Time
24.5.2 Predictive Analytics and Diagnostics
24.5.3 Sustainable Houses and Energy Efficiency
24.6 Challenges, Limitations, and Viable Solutions
24.6.1 Complication and Development Costs
24.6.2 Scalability and Integration Issues
24.6.3 Security and Data Privacy Worries
24.6.4 Low Understanding and Expertise
24.6.5 Clinical and Ethical Standards
24.7 Conclusion and Future Scope
References
25. Case Study on Biosensor-Based Neuromorphic Devices
Rajiv Iyer, Vedprakash C. Maralapalle and Poornima Mahesh
25.1 Introduction
25.1.1 Definition
25.1.2 Potential of Biosensors for Neuromorphic Devices
25.2 Design and Fabrication of Biosensor-Based Neuromorphic Devices
25.2.1 Selection of Biosensors
25.2.2 Development of Neural Network Architecture
25.2.2.1 Fundamental Principles
25.2.2.2 Key Components of Neural Network Architecture
25.2.2.3 Artificial Neurons and Design Neuron Models
25.2.2.4 Leaky Integrate-and-Fire Model
25.2.2.5 Hodgkin-Huxley Model
25.2.2.6 Learning Algorithms
25.2.2.7 On-Chip Learning Capabilities
25.2.3 Design Considerations for Biosensor Integration
25.2.4 Challenges and Limitations
25.3 Case Study: Biosensor-Based Neuromorphic Device for Specific Application
25.3.1 Case Study 1
25.3.1.1 Device Design, Implementation, and Performance
25.3.1.2 Potential Impact on Neuromorphic Engineering
25.3.2 Case Study 2
25.3.2.1 Description of Device Design, Implementation, and Performance
25.3.2.2 Potential Impact on Neuromorphic Engineering
25.4 Future Directions and Applications
25.4.1 Future Directions
25.4.2 Interdisciplinary Collaboration
25.4.3 Further Research
Conclusion
References
26. Case Study on Robotic Applications Based on Neuromorphic Engineering
Aghila, Sangeetha V. and Malini A.
26.1 Introduction to Neuromorphic Engineering
26.1.1 Concepts in Neuromorphic Engineering
26.1.2 Applications of Neuromorphic Engineering
26.1.3 Case Studies in Neuromorphic Engineering
26.2 Introduction about Robotic Applications
26.2.1 Key Areas of Robotic Applications
26.2.2 Additional Areas of Robotic Applications
26.2.3 Future Trends in Robotic Applications
26.3 Neuromorphic Engineering in Robotics
26.3.1 Introduction
26.3.2 Background
26.3.3 Key Applications
26.3.3.1 Autonomous Navigation
26.3.3.2 Human-Robot Interaction
26.3.3.3 Prosthetics Control
26.3.3.4 Advantages of Neuromorphic Robotics
26.3.4 Challenges and Future Directions
26.3.5 Conclusion
26.4 Autonomous Navigation in Robotics: A Neuromorphic Engineering Perspective
26.4.1 Introduction
26.4.2 Key Concepts in Neuromorphic Autonomous Navigation
26.4.2.1 Spiking Neural Networks (SNNs)
26.4.2.2 Event-Driven Processing
26.4.2.3 Sensory Integration
26.4.3 Applications of Neuromorphic Autonomous Navigation
26.4.3.1 Mobile Robots
26.4.3.2 Robotic Exploration
26.4.3.3 Smart Environments
26.4.4 Advantages of Neuromorphic Autonomous Navigation
26.4.5 Challenges and Future Directions
26.4.6 Conclusion
26.5 Human-Robot Interaction: A Neuromorphic Engineering Perspective
26.5.1 Introduction
26.5.2 Key Concepts in Neuromorphic Human-Robot Interaction
26.5.2.1 Spiking Neural Networks (SNNs)
26.5.2.2 Real-Time Processing
26.5.2.3 Multi-Modal Sensory Integration
26.5.3 Applications of Neuromorphic Human-Robot Interaction
26.5.3.1 Social Robots
26.5.3.2 Collaborative Robots (Cobots)
26.5.3.3 Interactive Learning Environments
26.5.4 Advantages of Neuromorphic Human-Robot Interaction
26.5.5 Challenges and Future Directions
26.5.6 Conclusion
26.6 Prosthetics Control: A Neuromorphic Engineering Perspective
26.6.1 Introduction
26.6.2 Key Concepts in Neuromorphic Prosthetics Control
26.6.2.1 Spiking Neural Networks (SNNs)
26.6.2.2 Brain-Computer Interfaces (BCIs)
26.6.2.3 Event-Driven Processing
26.6.3 Applications of Neuromorphic Prosthetics Control
26.6.3.1 Intuitive Movement Control
26.6.3.2 Adaptive Learning
26.6.3.3 Sensory Feedback
26.6.4 Advantages of Neuromorphic Prosthetics Control
26.6.5 Challenges and Future Directions
26.6.6 Conclusion
Bibliography
27. Case Study on Building Artificial Retinas Based on Neuromorphic Computing Systems
Kanimozhi N., S. Sujitha, Sarmila K. B. and Nithya Rekha Sivakumar
27.1 Introduction
27.1.1 What is the Purpose of Using Neuromorphic Systems?
27.2 How Neuromorphic Operates
27.2.1 The Characteristics of Von Neumann Systems
27.2.2 Characteristics of Neuromorphic Computing
27.3 Neuromorphic Architecture
27.3.1 Digital Computation
27.3.2 Spiking Neural Networks (SNNs)
27.3.3 Processing Information in Vast Parallel
27.3.4 Training and Modification
27.4 Architecture
27.4.1 Architecture Von Neumann
27.4.2 Classical Architecture
27.4.3 Real Architecture
27.5 Systems Interaction
27.5.1 Important Concepts and Principles of Neuromorphic Computing
27.6 Algorithms in Neuromorphic
27.6.1 Neuromorphic Computing Models
27.7 Neuron Models
27.7.1 Network Connection
27.8 Benefits of Neuromorphic Computing
27.8.1 Flexibility
27.8.2 Energy Effectiveness
27.8.3 Excellent Work
27.8.4 Processing
27.9 Challenges of Neuromorphic Computing
27.9.1 Reduced Accuracy
27.9.2 Absence of Norms and Benchmarks
27.9.3 Restricted Software and Accessibility
27.10 The Difference between Neuromorphic Computing and Traditional Computing
27.11 Common Use Cases for Neuromorphic Computing
27.11.1 Self-Driving Cars
27.11.2 Online Safety
27.11.3 AI Edge
27.11.4 Identification of Patterns
27.11.5 Automation
27.11.6 Neuromorphic Algorithms and Applications
27.12 Artificial Retinas Based on Neuromorphic Computing Systems
27.12.1 Components Involved in Artificial Vision
27.12.1.1 Array of Photodetectors
27.12.1.2 The Neuromorphic Processor Unit
27.12.1.3 Synapse Array
27.12.1.4 Connecting Circuits
27.12.1.5 Optional Flexible Substrate
27.13 General Implementation Features
27.13.1 The Process of Creating Neuromorphic Processing Units
27.13.2 Combining Synaptic Array
27.13.3 Circuit Interfacing and Control
27.13.4 Testing and System Collaboration
27.13.5 Optimization and Accuracy Assessment
27.14 Applications Using Photoelectric Systems for Neurological Sight
27.14.1 Fundamental Concepts of Architecture and Functions
27.15 Case Study
27.15.1 Retinal Diseases
27.15.2 The Role of Biology in Neuromorphic Technology
27.16 Human Vision
27.16.1 Spiking Neurons
27.16.2 Synapses
27.16.3 Spike-Based Neuromorphic Computing for Vision Modeled After the Brain
27.17 Neuromorphic Computing Systems Applications in Various Field
27.17.1 Diagnostic Medical Imaging
27.17.2 Adaptive Control in Robotics
27.17.3 Computational Intelligence
27.17.4 Pattern Identification
27.17.5 Analyzing the Real-Time Data
27.17.6 The Identification of Anomalies
27.17.7 Robotics
27.17.8 Fraud Identification
27.17.9 Predictive Servicing
References
28. Case Study on Intelligent Manufacturing Industries Automated with Neuromorphic Systems
S. Sujitha, N. Kanimozhi and Ahmad Alkhayyat
28.1 Introduction
28.2 Neuromorphic System in Medical Instrument Manufacturing
28.2.1 Neuromorphic Computing: Building Brains in Silicon
28.2.2 Neuromorphic Systems for Biohybrid Systems and Brain-Machine Interfaces
28.2.3 Neuromorphic Approaches for Bio Signal Analysis
28.2.4 Embracing the Future: Leveraging Neuromorphic Systems for the BFSI Industry
28.2.5 Harnessing the Power of Neuromorphic Computing and Spiking Neural Networks in the BFSI Industry
28.2.6 The Proliferation of Connected Devices – BFSI Industry Has Generated Massive Amounts of Data
28.2.7 TCS – Neuromorphic Computing: Ushering in AI Innovation in BFSI
28.2.8 Modeling and Simulation in Neuromorphic Systems
28.2.9 Epidemiological Simulations of Neuromorphic Systems
28.2.10 Simulating Neuromorphic Hardware
28.2.11 GPU-Enabled Simulation Framework – GeNN in Neuromorphic Systems
28.2.12 Modeling and Simulation in Neuroscience
28.2.13 Exploring the Intersection of Data Analytics and Neuromorphic Computing
28.2.14 Power Electronics in Neuromorphic Systems
28.3 Material Sciences and Neuromorphic Systems
28.4 The Future Enhancement of Computing: The Transformative Possible of Neuromorphic Systems
28.5 The Conclusion
Bibliography
29. Case Study on Neuromorphic Interface Equipped Bio-Signal Analysis Approach for Smart Brain Stroke Detection
K. Nithya, C.R. Dhivyaa, C. Sharmila and S. Anbukkarasi
29.1 Smart Brain Stroke Detection
29.2 Bio-Signal Analysis for Brain Stroke Detection
29.3 Neuromorphic Interfaces for Real-Time Signal Analysis
29.4 Neuromorphic Computing
Discussion
Conclusion
References
30. Role of Neuromorphic Computing Guidelines for Artificial Super Intelligence Systems
N. Saranya and N. Ramya
Introduction
Evolution from Classical Computing to Neuromorphic Paradigms
Neuroscience Fundamentals
Basic Inputs of Neuromorphic Computing in AI
Summary of Neuromorphic Inputs in AI
Applications
Guidelines for Artificial Super Intelligence System
Conclusion
Bibliography

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