Primer to Neuromorphic Computing
- 1st Edition - November 9, 2024
- Imprint: Academic Press
- Editors: Harish Garg, Jyotir Moy Chatterjee, R Sujatha, Shatrughan Modi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 1 4 8 0 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 1 4 8 2 - 0
Primer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientifi… Read more
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Request a sales quotePrimer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientific concepts for the research topic of neuromorphic computing, such as neurons as computational units, artificial intelligence, machine learning, and neuromorphic models. It also discusses the fundamental design method and organization of neuromorphic architecture.
Hardware for neuromorphic systems can be developed by exploiting the magnetic properties of different materials. These systems are more energy efficient and enable faster computation . Magnetic tunnel junctions and magnetic textures can be employed to act as neurons and synapses. Neuromorphic systems have general intelligence like humans as they can apply knowledge gained in one domain to other domains.
- Discusses potential neuromorphic applications in computing
- Presents current trends and models in neuromorphic computing and neural network hardware architectures
- Shows the development of novel devices and hardware to enable neuromorphic computing
- Offers information about computation and learning principles for neuromorphic systems
- Provides information about Neuromorphic implementations of neurobiological learning algorithms
- Discusses biologically inspired neuromorphic systems and devices (including adaptive bio interfacing and hybrid systems consisting of living matter and synthetic matter)
IT industry professionals, academic professors, research scholars, system modelling and simulation experts. This book on the neuromorphic computing can be offered as an elective course for graduate students
- Primer to Neuromorphic Computing
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1 Neuromorphic computing for machine learning: An overview
- Abstract
- Keywords
- 1 Introduction
- 2 Literature review
- 3 Methods and procedures
- 3.1 Dataset
- 3.2 Imbalance nature of the dataset
- 3.3 SMOTE
- 3.4 Artificial neural networks
- 3.5 Convolutional neural networks
- 3.6 Spiking neural networks
- 3.7 Modeling
- 4 Results and analysis
- 4.1 Results of experimental work
- 4.2 ROC curve
- 4.3 SHAP values
- 5 Conclusion
- 6 Key abbreviations
- References
- Chapter 2 Designing of GAN for a real-time image processing in neuromorphic system
- Abstract
- Keywords
- 1 Introduction
- 2 History of GANs
- 3 Working principle of GANs
- 4 Types and literature review of each GAN
- 4.1 Working of GAN in different applications—Type 1
- 4.2 Recent survey of Conditional GANs
- 4.3 Recent survey of Wasserstein GAN
- 4.4 Applications of GANs
- 4.5 GANs in real-time image processing
- 5 Literature review
- 6 Advantages of utilizing GANs in neuromorphic systems include
- 7 Disadvantages of employing GANs in neuromorphic systems include
- 8 Conclusion
- References
- Chapter 3 Review of existing neuromorphic systems
- Abstract
- Keywords
- 1 Introduction
- 2 Neuromorphic system
- 3 Neuromorphic in-memory computing using electrical synapse
- 3.1 Memristor
- 3.2 Advanced applications of neuromorphic vision sensors
- 4 Neuromorphic systems for audition
- 4.1 Edge computing—Powered by neuromorphic
- 5 Conclusion
- References
- Chapter 4 Neuromorphic system for cardiovascular disorders
- Abstract
- Keywords
- 1 Introduction
- 2 Neuromorphic systems
- 3 State of the art
- 4 Case study: Cardiovascular disease detection
- 4.1 Dataset
- 4.2 Preprocessing
- 4.3 The spiking neural networks
- 5 Conclusion
- References
- Chapter 5 Neuromorphic system for real-time healthcare applications
- Abstract
- Keywords
- 1 Introduction
- 2 A glimpse into the existing literature work
- 3 A case study—Seizure classification by analyzing EEG
- 3.1 The dataset and its description
- 3.2 The preprocessing of the data signals
- 3.3 The SNN model for seizure classification
- 4 Conclusion and future scope of the work
- References
- Chapter 6 Neomorphic home automation systems
- Abstract
- Keywords
- 1 Introduction
- 1.1 Objectives
- 1.2 Research gap
- 2 Neuromorphic systems for smart home devices architectures
- 2.1 Spiking neural networks (SNNs)
- 2.2 Reservoir computing (RC)
- 2.3 Neuromorphic hardware
- 2.4 Edge computing
- 2.5 Hybrid architectures
- 3 Neuromorphic systems for smart home devices real-time live applications with examples
- 4 Conclusion
- References
- Chapter 7 Neuromorphic systems for real-time image processing
- Abstract
- Keywords
- 1 Introduction to neuromorphic systems
- 1.1 Neuromorphic systems in image processing
- 1.2 Memristive system-based image processing technology
- 1.3 Image processing based on memristive systems
- 2 Biological background for neuromorphic computing
- 2.1 Human vision
- 2.2 Spiking neurons
- 2.3 Synapses
- 3 Neuromorphic system in real-time image processing
- 3.1 Event-based visual processing
- 4 Neuromorphic vision chips
- 4.1 Frame-driven vision sensors
- 4.2 Event-driven vision sensors
- 5 Spiking neural network models
- 5.1 Background
- 5.2 SNNs and object detection
- 6 Neuromorphic vision algorithms for attention and object recognition
- 7 Applications of neuromorphic system in real-time image processing
- 7.1 Robotics
- 7.2 Self-driving cars
- 7.3 Event vision sensors
- 7.4 Collaborative autonomous systems
- 8 Results and discussions
- 9 Conclusion
- References
- Chapter 8 Real-time visual data processing using neuromorphic systems
- Abstract
- Keywords
- 1 Introduction
- 2 Literature survey
- 3 Neuromorphic vision sensors
- 4 Neuromorphic vision chips
- 4.1 Frame-driven vision chips
- 4.2 Event-driven vision chips
- 5 Neuromemristive systems for visual data
- 6 Visual data analysis under neuromorphic computing
- 6.1 Spiking neural networks
- 6.2 Applications
- 7 Implementations of neuromorphic visual systems
- 7.1 Hardware systems
- 7.2 Software systems
- 8 Conclusions and future scope
- References
- Chapter 9 Future prospective of neuromorphic computing in artificial intelligence_ A review, methods, and challenges
- Abstract
- Keywords
- 1 Introduction
- 2 Overview of artificial intelligence
- 3 System for neuromorphic computing
- 4 Challenges of neuromorphic computing
- 5 Applications
- 6 Future scope
- 7 Conclusion
- References
- Chapter 10 Neuroscience-inspired facial mask recognition using MobileNet and computer vision in real-time video streaming
- Abstract
- Keywords
- 1 Introduction
- 2 Literature review
- 3 Accuracy of the model
- 4 Result and discussion
- 5 Merits of the proposed work
- 6 Conclusion
- 7 Future work
- References
- Chapter 11 A neuroinspired journey: Tracing the evolution and objectives of neuromorphic systems
- Abstract
- Keywords
- 1 Introduction
- 2 Origin of neuromorphic systems
- 3 Neuromorphic computing history
- 4 What is new in the neuromorphic concept?
- 5 Development of a neuromorphic framework
- 6 The goals of a neuromorphic framework
- 7 How neuromorphic frameworks taking motivation from the functions of the human mind?
- 8 Scope of neuromorphic computing
- 9 Big players in the neuromorphic computer market
- 10 Benchmarks for progress in neuromorphic computing
- 11 Neuromorphic computing thematic areas
- 12 Challenges for neuromorphic computing
- 13 Social and industrial concerns
- 14 Conclusion
- References
- Chapter 12 Deploying shark smell optimization algorithm with neuromorphic computing for prediction of COVID-19 disease
- Abstract
- Keywords
- 1 Introduction
- 2 Background
- 3 Proposed mechanism
- 4 Experimental results
- 5 Future directions
- 6 Conclusion
- References
- Index
- Edition: 1
- Published: November 9, 2024
- Imprint: Academic Press
- No. of pages: 370
- Language: English
- Paperback ISBN: 9780443214806
- eBook ISBN: 9780443214820
HG
Harish Garg
Dr. Garg is Associate Professor of Mathematics at Thapar Institute of Engineering and Technology, Patiala, Punjab, India. He is the recipient of the Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 – 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN).
Dr. Garg's research interests include computational intelligence, multi-criteria decision making, evolutionary algorithms, reliability analysis, expert systems, and decision support systems, computing with words, and soft computing. He has authored more than 400 papers published in refereed international journals. He has also authored seven book chapters. He has also edited 8 books from Elsevier, Springer, and other publishers. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, and CAAI Transactions on Intelligence Technology.
Affiliations and expertise
Associate Professor, School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaJM
Jyotir Moy Chatterjee
Jyotir Moy Chatterjee is currently an Assistant Professor in Department of Computer Science and Engineering at Graphic Era (Deemed to be University), in Dehradun, India. He also serves as a Visiting Faculty member in Information Technology at Lord Buddha Education Foundation, which is affiliated with the Asia Pacific University of Technology & Innovation in Kathmandu, Nepal. His research interests focus on advancements in Machine Learning and Deep Learning.
Affiliations and expertise
Assistant Professor, Department of CSE, Graphic Era University, Dehradun, IndiaRS
R Sujatha
Dr. R. Sujatha received the B.E. degree in computer science from Madras University, in 2001, the M.E. degree in computer science from Anna University, in 2009, with university ninth rank, the master’s degree in financial management from Pondicherry University, in 2005, and the Ph.D. degree in data mining from the Vellore Institute of Technology (VIT), Vellore, in 2017. She has 15 years of teaching experience and has been serving as an Associate Professor with the School of Information Technology and Engineering, VIT. She has organized and attended several workshops and faculty development programs. She actively involves herself in the growth of the institute by contributing to various committees at both academic and administrative levels. She gives technical talks in colleges for the symposium and various sessions. She acts as an advisory, editorial member, and technical committee member in conferences conducted in other educational institutions and in-house too. She has published a book Software Project Management for college students. Also, she has published research articles and papers in reputed journals. She used to guide projects for undergraduate and postgraduate students and currently guides doctoral students. She is interested in learning upcoming things and gets herself acquainted with the student’s level. Her areas of research interests include data mining, machine learning, software engineering, soft computing, big data, deep learning, and blockchain.
Affiliations and expertise
Associate Professor, School of Information Technology Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IndiaSM
Shatrughan Modi
Dr. Shatrughan Modi is an Assistant Professor at Department of Computer Science, Indian Institute of Information Technology Una, India. Prior to that he worked at a Computer Science and Engineering Department of Thapar University, Patiala. He has more than 9 years of teaching experience and 2 years of industry experience. Her areas of research interests include data mining, neural network, pattern recognition, Autonomous vehicles, software engineering, machine learning, deep learning.
Affiliations and expertise
Assistant Professor, Department of Computer Science, Indian Institute of Information Technology, Una, Himachal Pradesh, IndiaRead Primer to Neuromorphic Computing on ScienceDirect