Ai-Augmented Digital Logic Design: From Elementary To Master

Not just another HDL course—use AI to learn, design, simulate, and master digital logic design, from beginners to profes

What you'll learn

Understand digital logic fundamentals and HDL basics through AI-guided exploration.

Apply AI tools to write, simulate, and debug Verilog/VHDL code using prompt engineering.

Analyze AI-generated HDL for correctness, structure, and synthesis compatibility.

Create testbenches and waveform analyses using AI-generated prompts and simulation tools.

Evaluate synthesis results using Yosys and refine HDL design with AI-based optimization.

Construct a complete, documented HDL project portfolio using AI for learning and delivery.

Requirements

Basic familiarity with digital logic concepts is helpful but not mandatory; no coding or HDL knowledge is required.

Description

Learn how to use Ai in commercial Digital Logic Design process.Completly outcome oriented courses focused to help learners build career in Artificial Intelligent for Chip Design.# Unique Features of the Course  1. AI-Augmented Learning Model   - Teaches how to learn HDL using AI, not by memorizing syntax.   - Uses ChatGPT/Gemini for code generation, debugging, and concept explanation.   - Promotes logic-first, not syntax-first learning.2. Cloud-Based, No Installation Required   - Entire course runs on Google Colab.   - No need to install GHDL, Yosys, or GTKWave locally.   - Works on low-spec laptops and all OS platforms.3. Hands-On from Day 1   - Real code, real simulation, and real outputs from the second module.   - All lectures include AI prompts, code, and live demo.4. No Coding Background Required   - Ideal for first-time learners of using AI in Digital logic Design.   - Natural language prompts help generate Verilog/VHDL code.   - Accessible to ECE, EE, CS, Physics, and Mechatronics students.5. Structured, Rubric-Based Assignments   - Each module ends with a fixed-scope assignment.   - Includes AI prompt, expected output, and evaluation rubric.   - Suitable for Self-assement.6. Scaffolded, Progressive Learning Path   - 16 modules (1 Intro + 14 Core + 1 Conclusion).   - Gradually builds from simple gates to FSM and capstone project.   - AI is used consistently as a learning co-pilot.7. Portfolio-Driven Learning   - Students build and share a GitHub/Notion-based portfolio.   - Outputs are verified, simulated, and documented.   - Helpful for internships, job interviews, and research.8. Industry-Relevant, Tool-Focused    - Tools used: ChatGPT, Gemini, Colab, Yosys, GHDL, Icarus Verilog, GTKWave.    - Same tools used in real-world digital and embedded system design.9. AI Prompts as Learning Assets    - Course includes reusable prompt templates.    - Prompts cover logic blocks, debugging, testbenches, and synthesis.    - Promotes AI-driven problem-solving.10. Career-Oriented Outcomes    - Prepares learners for roles like:      - FPGA/RTL engineer      - Digital verification intern      - Embedded systems designer      - Research assistant      - Freelance HDL developer# Course Modules: 1. Introduction to AI-Assisted Digital Logic Design – Course overview, tools, and AI workflow basics.2. Cloud-Based Workflow Setup – Using Google Colab, GitHub, and open-source HDL tools without installation.3. Learning HDL Through AI – Using AI prompts to understand HDL syntax, concepts, and debugging.4. Prompt-Driven HDL Generation – Crafting reusable AI prompt templates for common digital circuits.5. AI-Generated Testbenches – Creating, simulating, and refining testbenches with AI guidance.6. AI-Assisted Simulation & Debugging – Running simulations, analyzing waveforms, and fixing bugs with AI help.7. AI + Linting and Clean Code – Using Verilator and AI to improve HDL code quality.8. AI-Assisted Synthesis with Yosys – RTL-to-gate-level synthesis, netlist analysis, and optimization.9. FSM Design Using AI Prompts – Designing and simulating Moore and Mealy state machines.10. Arithmetic Circuits with AI – Building ALUs, adders, shifters, and other logic blocks.11. Documentation with AI Assistance – Generating diagrams, reports, and technical documentation.12. AI-Powered Project Showcase – Turning completed projects into portfolio-ready assets.13. Capstone Planning with AI – Selecting, scoping, and planning a complete HDL project.14. Capstone Execution & Showcase – Building, simulating, and presenting the final project.15. Conclusion & Future Pathways – Recap, reflection, and exploring advanced AI + HDL learning paths.

Overview

Section 1: 01. Introduction to AI-Assisted Design

Lecture 1 Introduction to AI-Assisted Design - Getting Started with AI-Assisted Digital De

Lecture 2 Course Objectives, Outcomes & Structure

Section 2: 02. Cloud-Based Workflow Setup

Lecture 3 Why Use Cloud + Open Tools for HDL Design?

Lecture 4 Setting up Github Repo for the course

Lecture 5 Installing GHDL, Icarus, and Yosys in Google Colab

Lecture 6 Your First HDL Prompt: AND Gate + Testbench

Section 3: 03. Learning HDL Through AI

Lecture 7 How to Ask AI to Teach You HDL

Lecture 8 Explain Syntax with AI Examples

Lecture 9 Debugging HDL Using AI Explanations

Lecture 10 From Truth Table to Working HDL Prompt

Lecture 11 AI-Augmented Complex Combinational Circuit Design

Section 4: 04. Prompt-Driven HDL Generation

Lecture 12 Debugging HDL using AI explanations Compile, Identify, and Fix Errors

Lecture 13 Reusable Scripts and Prompt Templates for HDL Automation

Lecture 14 4-Bit Counter with Reusable Templates

Lecture 15 AI Reusable Scripts and templates for Complex Circuits

Undergraduate students, hobbyists, educators, and professionals interested in learning or accelerating digital logic design using AI—no prior HDL experience required.