Machine Learning Techniques for VLSI Chip Design

✍ Scribed by Abhishek Kumar, Suman Lata Tripathi, K. Srinivasa Rao

• Publisher: Wiley-Scrivener
• Year: 2023
• Tongue: English
• Leaves: 239
• Category: Library

✦ Synopsis

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 learning techniques for VLSI chip design.

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 arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and 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.

✦ Table of Contents

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 & 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 < 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
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 & 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