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This course will be used to consolidate the learning resources that our trainees had been using for their processor design project. Future trainees will find all learning materials, videos, worksheets etc at one place.

Course Sections:

1. Resources

2. RISC V

3. Designing a Single Cycle processor

4. Project Submission

Course Sections:

1. Orientation

2. Getting Started

3. Resources

4. Module 1: System Verilog

5. Module 2: UVM Basics

6. Module 3: UVM Advanced

7. Module 4: SoC-IP Verification (Capstone)

Course Sections:

1. Getting Started (Basics)

2. Modules & Classes

3. Arrays

4. Randomization

5. Threads & Interprocess Communication

6. Testdef / Enum

7. Time Regions / Simulation Cycles

8. TestBench Basics

9. Verifications Basics Live Session

10. Writing complete test bench

11. Case Study: Memory Model

12. Coverage

13. Test Planning

14. Grand Quiz

15. Final Project

Course Sections:

1. What is a vector processor

2. RVV Basic Concept and CSRs

3. RVV load/store and arithmetic instructions

Trainees need to take this course at the time of transitioning out of this course.

Course Sections:

1. Bind Construct

2. Direct Programming Interface

Course Sections:

1. Learning Resources

2. RAL (Register Abstraction Layer)

3. Project

Course Sections:

1. UVM Slides 

2. Intended Audience

3. Pre-Assessment

4. Introduction to UVM

5. UVM Concepts

6. UVM Basics Live Session

7. Ready to start coding?

8. UVM Live Session 

9. A peek into real assignments

10. Capstone project

11. Final Assessment & Evaluation

This course has 3 artifacts for conducting one on one meetings. 

1. A set of slides that explains the purpose, principle and behaviors in One-on-One meetings. 

2. When a person is assigned to a manager, their first meeting should be an introductory meeting, it should not be classified as a One-on-One meeting.  A checklist in this course shows what is to be covered in this introductory meeting. 

3. For doing a one-on-one meeting, managers should use the worksheet that has been provided and improvise as per your project and resource, while ensuring that the core structure remains in place (growth, commitment, alignment) 

Course Sections:

1. Basics of Computer Vision (Beginner Level)

• Introduction to computer vision and its applications
• Image representation: pixels, grayscale, RGB 
• Basic image processing: resize, crop, rotate 
• Basic Python programming 
• Introduction to OpenCV: loading, displaying images 
• Basic image transformations: scaling, translation, rotation, erosion, manipulation, histogram equilization, dilation, opening, and closing etc
• Filtering & Convolutions: blur, sharpen, edge detection (Sobel, Canny)

Course Sections:

1. Python: Basic Constructs

2. Python: Classes

3. Image Processing: Foundation

4. Image Processing: Point Operations

5. Image Processing: Linear Filters

6. Image Processing: Non-Linear Filters

7. Final Assessment

This course is designed to provide a solid foundation in AI and deep learning over three weeks, with each week building on the knowledge gained in the previous one. By the end of the course, participants will have a comprehensive understanding of the core concepts, tools, and techniques in AI and deep learning, along with practical experience in implementing and optimizing models.

1. Overview of AI, ML, and Deep Learning

2. Types of Machine Learning (Supervised, Unsupervised, Reinforcement) 

3. Basic Concepts: Features, Labels, Training, and Testing 

4. Introduction to Python for ML (NumPy, Pandas, Matplotlib) 

5. Simple Linear Regression and Classification 

6. Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)

7. Introduction to Neural Networks 

Course Sections:

1. Setting up your computer for the Course

2. Python Fundamentals: Basic Construct (For Pre-Assessment)

3. Pre-Assessment

4. Advanced Function Techniques

5. Iterators and Generators

6. Classes

7. Regular Expressions (Regex)

8. Virtual Environments

9. Error Handling

10. Grand Assessment

Course Sections:

1. Foundation

2. Image Transformations

3. Point Operations

4. Linear Filters

5. Non-Linear Filters

Course Sections:

1. Deep Neural Networks

2. Tuning Neural Network

3. Tuning Hyperparameters and Multiclass Classification

4. Convolutional Neural Network

5. State of Art CNN Models

6. CNN Applications

Course Sections:

1. Regression

2. Classification

3. Unsupervised Learning

Course Sections:

1. LLVM IR Structure and IR Instructions

2. Analysis Passes

3. Transformation Passes

4. Project

5. Final Presentation

Course Sections:

1. Introduction to C++

2. Basics of C++

3. Object-Oriented Programming Basics

4. Inheritance and Polymorphism

5. Overloading

6. STL (Standard Template Library)

7. Templates

8. File Handling

9. Exception Handling

10. Concurrency and Threading

11. Project

Course Sections:

1. C Language Features

2. Multi-Threading in C

3. Memory Management and File IO

4. Debugging

5. Data Structures and Algorithms

6. Grand Assessment

Course Sections:

1. RISC-V Arch Test

1.1 RISC-V-Privilege Spec

1.2 Tasks

2. Setup Content

2.1 Getting Started with RISC-V Compliance

2.2 Task: Test Plan Writing

Course Sections:

1. Lectures & Labs (For Reference)

2. SV for Design - Assignments, Lab Manuals & Worksheets

3. Grand Assessment

Course Sections:

1. Basics: RTL To GDS

2. Advance Synthesis

3. Grand Assessment

Course Sections:

1. Setting up your computer for the Course

2. Python Fundamentals: Basic Construct (For Pre-Assessment)

3. Pre-Assessment

4. Advanced Function Techniques

5. Iterators and Generators

6. Classes

7. Regular Expressions (Regex)

8. Grand Assessment