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Machine Learning Techniques for VLSI Chip Design

Edited by Abhishek Kumar, Suman Lata Tripathi, and K. Srinivasa Rao
Copyright: 2023   |   Status: Published
ISBN: 9781119910398  |  Hardcover  |  
216 pages
Price: $225 USD
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One Line Description
This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design.

Audience
Engineers, designers, researchers, and undergraduate, postgraduate and research students and faculty working in the areas of artificial intelligence, machine learning models, architectures and their applications

Description
Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arises when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, implementation of the computational architecture for ML-DL.

The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.

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Author / Editor Details
Abhishek Kumar, PhD, is an associate professor at and obtained his PhD in the area of VLSI Design for Low Power and Secured Architecture from Lovely Professional University, India. With over 11 years of academic experience, he has published more than 30 research papers and proceedings in scholarly journals. He has also published nine book chapters and one authored book. He has worked as a reviewer and program committee member and editorial board member for academic and scholarly conferences and journals, and he has 11 patents to his credit.

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.

K. Srinivasa Rao, PhD, is a professor and Head of Microelectronics Research Group, Department of Electronics and Communication Engineering at the Koneru Lakshmaiah Education Foundation, India. He has earned multiple awards for his scholarship and has published more than 150 papers in scientific journals and presented more than 55 papers at scientific conferences around the world.

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Table of Contents
List of Contributors
Preface
1. Applications of VLSI Design in Artificial Intelligence and Machine Learning
Imran Ullah Khan, Nupur Mittal and Mohd. Amir Ansari
1.1 Introduction
1.2 Artificial Intelligence
1.3 Artificial Intelligence & VLSI (AI and VLSI)
1.4 Applications of AI
1.5 Machine Learning
1.6 Applications of ML
1.6.1 Role of ML in Manufacturing Process
1.6.2 Reducing Maintenance Costs and Improving Reliability
1.6.3 Enhancing New Design
1.7 Role of ML in Mask Synthesis
1.8 Applications in Physical Design
1.8.1 Lithography Hotspot Detection
1.8.2 Pattern Matching Approach
1.9 Improving Analysis Correlation
1.10 Role of ML in Data Path Placement
1.11 Role of ML on Route Ability Prediction
1.12 Conclusion
References
2. Design of an Accelerated Squarer Architecture Based on Yavadunam Sutra for Machine Learning
A.V. Ananthalakshmi, P. Divyaparameswari and P. Kanimozhi
2.1 Introduction
2.2 Methods and Methodology
2.2.1 Design of an n-Bit Squaring Circuit Based on (n-1)-Bit Squaring Circuit Architecture
2.2.1.1 Architecture for Case 1: A < B
2.2.1.2 Architecture for Case 2: A > B
2.2.1.3 Architecture for Case 3: A = B
2.3 Results and Discussion
2.4 Conclusion
References
3. Machine Learning–Based VLSI Test and Verification
Jyoti Kandpal
3.1 Introduction
3.2 The VLSI Testing Process
3.2.1 Off-Chip Testing
3.2.2 On-Chip Testing
3.2.3 Combinational Circuit Testing
3.2.3.1 Fault Model
3.2.3.2 Path Sensitizing
3.2.4 Sequential Circuit Testing
3.2.4.1 Scan Path Test
3.2.4.2 Built-In-Self Test (BIST)
3.2.4.3 Boundary Scan Test (BST)
3.2.5 The Advantages of VLSI Testing
3.3 Machine Learning’s Advantages in VLSI Design
3.3.1 Ease in the Verification Process
3.3.2 Time-Saving
3.3.3 3Ps (Power, Performance, Price)
3.4 Electronic Design Automation (EDA)
3.4.1 System-Level Design
3.4.2 Logic Synthesis and Physical Design
3.4.3 Test, Diagnosis, and Validation
3.5 Verification
3.6 Challenges
3.7 Conclusion
References
4. IoT-Based Smart Home Security Alert System for Continuous Supervision
Rajeswari, N. Vinod Kumar, K. M. Suresh, N. Sai Kumar and K. Girija Sravani
4.1 Introduction
4.2 Literature Survey
4.3 Results and Discussions
4.3.1 Raspberry Pi-3 B+Module
4.3.2 Pi Camera
4.3.3 Relay
4.3.4 Power Source
4.3.5 Sensors
4.3.5.1 IR & Ultrasonic Sensor
4.3.5.2 Gas Sensor
4.3.5.3 Fire Sensor
4.3.5.4 GSM Module
4.3.5.5 Buzzer
4.3.5.6 Cloud
4.3.5.7 Mobile
4.4 Conclusions
References
5. A Detailed Roadmap from Conventional-MOSFET to Nanowire-MOSFET
P. Kiran Kumar, B. Balaji, M. Suman, P. Syam Sundar, E. Padmaja and K. Girija Sravani
5.1 Introduction
5.2 Scaling Challenges Beyond 100nm Node
5.3 Alternate Concepts in MOFSETs
5.4 Thin-Body Field-Effect Transistors
5.4.1 Single-Gate Ultrathin-Body Field-Effect Transistor
5.4.2 Multiple-Gate Ultrathin-Body Field-Effect Transistor
5.5 Fin-FET Devices
5.6 GAA Nanowire-MOSFETS
5.7 Conclusion
References
6. Gate All Around MOSFETs-A Futuristic Approach
Ritu Yadav and Kiran Ahuja
6.1 Introduction
6.1.1 Semiconductor Technology: History
6.2 Importance of Scaling in CMOS Technology
6.2.1 Scaling Rules
6.2.2 The End of Planar Scaling
6.2.3 Enhance Power Efficiency
6.2.4 Scaling Challenges
6.2.4.1 Poly Silicon Depletion Effect
6.2.4.2 Quantum Effect
6.2.4.3 Gate Tunneling
6.2.5 Horizontal Scaling Challenges
6.2.5.1 Threshold Voltage Roll-Off
6.2.5.2 Drain Induce Barrier Lowering (DIBL)
6.2.5.3 Trap Charge Carrier
6.2.5.4 Mobility Degradation
6.3 Remedies of Scaling Challenges
6.3.1 By Channel Engineering (Horizontal)
6.3.1.1 Shallow S/D Junction
6.3.1.2 Multi-Material Gate
6.3.2 By Gate Engineering (Vertical)
6.3.2.1 High-K Dielectric
6.3.2.2 Metal Gate
6.3.2.3 Multiple Gate
6.4 Role of High-K in CMOS Miniaturization
6.5 Current Mosfet Technologies
6.6 Conclusion
References
7. Investigation of Diabetic Retinopathy Level Based on Convolution Neural Network Using Fundus Images
K. Sasi Bhushan, U. Preethi, P. Naga Sai Navya, R. Abhilash, T. Pavan and K. Girija Sravani
7.1 Introduction
7.2 The Proposed Methodology
7.3 Dataset Description and Feature Extraction
7.3.1 Depiction of Datasets
7.3.2 Preprocessing
7.3.3 Detection of Blood Vessels
7.3.4 Microaneurysm Detection
7.4 Results and Discussions
7.5 Conclusions
References
8. Anti-Theft Technology of Museum Cultural Relics Using RFID Technology
B. Ramesh Reddy, K. Bhargav Manikanta, P.V.V.N.S. Jaya Sai, R. Mohan Chandra, M. Greeshma Vyas and K. Girija Sravani
8.1 Introduction
8.2 Literature Survey
8.3 Software Implementation
8.4 Components
8.4.1 Arduino UNO
8.4.2 EM18 Reader Module
8.4.3 RFID Tag
8.4.4 LCD Display
8.4.5 Sensors
8.4.5.1 Fire Sensor
8.4.5.2 IR Sensor
8.4.6 Relay
8.5 Working Principle
8.5.1 Working Principle
8.6 Results and Discussions
8.7 Conclusions
References
9. Smart Irrigation System Using Machine Learning Techniques
B. V. Anil Sai Kumar, Suryavamsham Prem Kumar, Konduru Jaswanth, Kola Vishnu and Abhishek Kumar
9.1 Introduction
9.2 Hardware Module
9.2.1 Soil Moisture Sensor
9.2.2 LM35-Temperature Sensor
9.2.3 POT Resistor
9.2.4 BC-547 Transistor
9.2.5 Sounder
9.2.6 LCD 16x2
9.2.7 Relay
9.2.8 Push Button
9.2.9 LED
9.2.10 Motor
9.3 Software Module
9.3.1 Proteus Tool
9.3.2 Arduino Based Prototyping
9.4 Machine Learning (Ml) Into Irrigation
9.5 Conclusion
References
10. Design of Smart Wheelchair with Health Monitoring System
Narendra Babu Alur, Kurapati Poorna Durga, Boddu Ganesh, Manda Devakaruna, Lakkimsetti Nandini, A. Praneetha, T. Satyanarayana and K. Girija Sravani
10.1 Introduction
10.2 Proposed Methodology
10.3 The Proposed System
10.4 Results and Discussions
10.5 Conclusions
References
11. Design and Analysis of Anti-Poaching Alert System for Red Sandalwood Safety
K. Rani Rudrama, Mounika Ramala, Poorna sasank Galaparti, Manikanta Chary Darla, Siva Sai Prasad Loya and K. Srinivasa Rao
11.1 Introduction
11.2 Various Existing Proposed Anti-Poaching Systems
11.3 System Framework and Construction
11.4 Results and Discussions
11.5 Conclusion and Future Scope
References
12. Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748
T. Anil Raju, K. Srihari Reddy, Sk. Arifulla Rabbani, G. Suresh, K. Saikumar Reddy and K. Girija Sravani
12.1 Introduction
12.2 Image Processing
12.2.1 Image Acquisition
12.2.2 Image Segmentation Method
12.3 TMS320C6748 DSP Processor
12.4 Code Composer Studio
12.5 Morphological Image Segmentation
12.5.1 Optimization
12.6 Results and Discussions
12.7 Conclusions
References
13. Design Challenges for Machine/Deep Learning Algorithms
Rajesh C. Dharmik and Bhushan U. Bawankar
13.1 Introduction
13.2 Design Challenges of Machine Learning
13.2.1 Data of Low Quality
13.2.2 Training Data Underfitting
13.2.3 Training Data Overfitting
13.2.4 Insufficient Training Data
13.2.5 Uncommon Training Data
13.2.6 Machine Learning Is a Time-Consuming Process
13.2.7 Unwanted Features
13.2.8 Implementation is Taking Longer Than Expected
13.2.9 Flaws When Data Grows
13.2.10 The Model’s Offline Learning and Deployment
13.2.11 Bad Recommendations
13.2.12 Abuse of Talent
13.2.13 Implementation
13.2.14 Assumption are Made in the Wrong Way
13.2.15 Infrastructure Deficiency
13.2.16 When Data Grows, Algorithms Become Obsolete
13.2.17 Skilled Resources are Not Available
13.2.18 Separation of Customers
13.2.19 Complexity
13.2.20 Results Take Time
13.2.21 Maintenance
13.2.22 Drift in Ideas
13.2.23 Bias in Data
13.2.24 Error Probability
13.2.25 Inability to Explain
13.3 Commonly Used Algorithms in Machine Learning
13.3.1 Algorithms for Supervised Learning
13.3.2 Algorithms for Unsupervised Learning
13.3.3 Algorithm for Reinforcement Learning
13.4 Applications of Machine Learning
13.4.1 Image Recognition
13.4.2 Speech Recognition
13.4.3 Traffic Prediction
13.4.4 Product Recommendations
13.4.5 Email Spam and Malware Filtering
13.5 Conclusion
References
About the Editors
Index

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