Machine learning techniques for VLSI chip design

Machine learning techniques for VLSI chip design

MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine...

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Detalles Bibliográficos
Otros Autores: Kumar, Abhishek, editor (editor), Tripathi, Suman Lata, editor, Srinivasa Rao, K. (Electronic engineer), editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ ; Beverly, MA : John Wiley & Sons, Inc [2023]
Materias:
Integrated circuits > Very large scale integration > Design > Data processing.
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757935206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • List of Contributors
  • Preface
  • Chapter 1 Applications of VLSI Design in Artificial Intelligence and Machine Learning
  • 1.1 Introduction
  • 1.2 Artificial Intelligence
  • 1.3 Artificial Intelligence &amp
  • 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
  • Chapter 2 Design of an Accelerated Squarer Architecture Based on Yavadunam Sutra for Machine Learning
  • 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 &lt
  • B
  • 2.2.1.2 Architecture for Case 2: A &gt
  • B
  • 2.2.1.3 Architecture for Case 3: A = B
  • 2.3 Results and Discussion
  • 2.4 Conclusion
  • References
  • Chapter 3 Machine Learning-Based VLSI Test and Verification
  • 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
  • Chapter 4 IoT-Based Smart Home Security Alert System for Continuous Supervision
  • 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 &amp
  • 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
  • Chapter 5 A Detailed Roadmap from Conventional-MOSFET to Nanowire-MOSFET
  • 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
  • Chapter 6 Gate All Around MOSFETs-A Futuristic Approach
  • 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
  • Chapter 7 Investigation of Diabetic Retinopathy Level Based on Convolution Neural Network Using Fundus Images
  • 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
  • Chapter 8 Anti-Theft Technology of Museum Cultural Relics Using RFID Technology
  • 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
  • Chapter 9 Smart Irrigation System Using Machine Learning Techniques
  • 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
  • Chapter 10 Design of Smart Wheelchair with Health Monitoring System
  • 10.1 Introduction
  • 10.2 Proposed Methodology
  • 10.3 The Proposed System
  • 10.4 Results and Discussions
  • 10.5 Conclusions
  • References
  • Chapter 11 Design and Analysis of Anti-Poaching Alert System for Red Sandalwood Safety
  • 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
  • Chapter 12 Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748
  • 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
  • Chapter 13 Design Challenges for Machine/Deep Learning Algorithms
  • 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
  • EULA.

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