MASTER AI CHIP DESIGN & EDA AUTOMATION
Apply AI, Python & Open-Source Tools to Modern Chip Design
Learn to automate digital chip-design workflows using Python, open-source Electronic Design Automation (EDA) tools, machine learning fundamentals and reproducible engineering pipelines. Explore RTL-to-GDSII implementation, design-space exploration, Power-Performance-Area (PPA) analysis and AI-assisted design optimisation through practical projects.
- 6/8 Months
- 200+ Practical Hours
- Live Online Training
- Open-Source EDA & AI-Based Engineering Labs
What You Will Learn
Semiconductor & AI Chip Design Fundamentals
Understand the engineering behind modern digital chips.
Build the foundation required to understand digital hardware design, chip implementation and the role of automation in semiconductor engineering.
- Semiconductor and integrated circuit fundamentals
- Digital design and ASIC architecture
- CPU, GPU and AI accelerator concepts
- RTL and digital hardware design fundamentals
- Introduction to Electronic Design Automation (EDA)
- Chip-design lifecycle and engineering workflows
- Logic synthesis and physical implementation concepts
- Power, Performance and Area (PPA) fundamentals
- Introduction to design-space exploration
- AI applications in semiconductor engineering
Python for Chip Design Automation
Use programming to automate repetitive engineering tasks.
Learn how Python can connect design tools, process engineering data and automate chip-design experiments.
- Python fundamentals for engineering
- Variables, functions and data structures
- File and directory automation
- Working with RTL, configuration and log files
- Running external tools from Python
- Processing synthesis and implementation reports
- Extracting timing, area and power-related metrics
- Generating tables and engineering reports
- Automating repeated design experiments
- Building reusable automation scripts
Linux, Git & Reproducible EDA Workflows
Build a reliable engineering environment.
Learn to organise design projects, execute tools and maintain repeatable workflows using open-source development practices.
- Linux command-line fundamentals
- Bash scripting
- Git and GitHub
- Project and source-code organisation
- Configuration and parameter management
- Environment and dependency management
- Build automation fundamentals
- Docker-based tool environments
- Execution logs and error handling
- Reproducible experiments and documentation
Open-Source EDA & RTL-to-GDSII Flow
Understand how digital designs move through the implementation pipeline.
Work with open-source tools to explore the flow from synthesizable RTL to physical layout.
- Introduction to Yosys synthesis
- RTL compilation and design preparation
- Logic synthesis and netlist generation
- OpenROAD implementation flow
- Floorplanning and placement concepts
- Clock-tree synthesis fundamentals
- Routing and physical implementation concepts
- KLayout layout visualisation
- Design-rule and implementation reports
- Analysis of RTL-to-GDSII flow results
Automated Design-Space Exploration
Explore how engineering choices affect chip implementation results.
Build automation workflows that execute multiple design configurations and compare the resulting metrics.
- Understand design parameters and constraints
- Create parameterised experiment configurations
- Automate multiple synthesis runs
- Automate supported physical-design runs
- Collect area and timing metrics
- Compare implementation results
- Analyse trade-offs between design configurations
- Build experiment summary tables
- Identify promising design configurations
- Develop repeatable design-space exploration scripts
Machine Learning for Chip Design Optimisation
Explore how machine learning can assist engineering decisions.
Learn the fundamentals of applying data analysis and ML methods to design metrics and optimisation problems.
- Python data analysis fundamentals
- Preparing datasets from EDA reports
- Feature selection and engineering
- Training and testing ML models
- Regression for metric prediction
- Evaluating prediction accuracy
- Predicting supported PPA metrics
- Comparing predicted and actual results
- Hyperparameter tuning fundamentals
- Understanding the limitations of ML-based optimisation
AI-Assisted EDA & Intelligent Automation
Explore AI techniques for improving engineering workflows.
Understand how AI and automation can support design exploration, report analysis and engineering productivity without treating AI-generated results as automatically correct.
- Introduction to AI-assisted EDA
- LLM applications in engineering workflows
- AI-assisted RTL and script review concepts
- Automated report summarisation
- Rule-based and ML-based optimisation
- Building controlled Python automation pipelines
- Using AI to assist failure and log analysis
- Validating generated code and recommendations
- Integrating AI-assisted steps into design workflows
- Comparing automated results against tool-generated evidence
End-to-End AI-Driven EDA Automation Project
Build an engineering pipeline that connects design tools, automation and data analysis.
Apply your learning to a project that executes supported EDA workflows, collects results and evaluates design alternatives.
- Select a suitable digital design and baseline configuration
- Prepare the RTL-to-GDSII execution environment
- Automate the design flow using scripts
- Collect area and timing results
- Run multiple design configurations
- Analyse results using Python
- Build a baseline ML model for a selected metric
- Compare model predictions with actual results
- Generate a final engineering dashboard or report
- Document limitations, findings and next steps
Hands-On AI & EDA Automation Practice
From Digital Design to AI-Assisted EDA Automation
Practise the workflow involved in connecting digital hardware design, EDA tools, engineering automation and data-driven optimisation.
Chip Design Requirements & Workflow Planning
- Understand the design objective
- Identify the digital design and its constraints
- Review the available RTL and configuration files
- Identify the required EDA tools and workflow stages
- Define the metrics to collect and compare
Python & Engineering Environment Setup
- Prepare the Linux-based development environment
- Organise project files and source code
- Develop Python scripts for file processing
- Use Git to manage changes
- Create repeatable execution and logging workflows
Open-Source EDA Flow Integration
- Prepare a supported RTL design
- Run synthesis using Yosys
- Execute a supported OpenROAD implementation flow
- Inspect netlists, logs and implementation reports
- Understand the flow's inputs, outputs and failure points
Design Automation & Regression Execution
- Create parameterised run configurations
- Execute tools through Python or shell scripts
- Automate multiple design runs
- Capture execution status and error logs
- Produce a consolidated run summary
PPA Metrics & Design-Space Exploration
- Extract supported area and timing metrics
- Organise results into structured datasets
- Compare different design configurations
- Identify trade-offs between implementation results
- Generate visual comparisons and summary reports
Machine Learning & Predictive Analysis
- Prepare an experimental dataset
- Select relevant design features
- Train a baseline regression model
- Evaluate prediction accuracy on held-out data
- Compare predicted metrics with actual EDA results
AI-Assisted Engineering Workflows
- Use AI to assist with selected script-writing tasks
- Summarise tool logs and engineering reports
- Review AI-generated recommendations
- Validate changes using the actual EDA flow
- Record verified outcomes and limitations
Record verified outcomes and limitations
- Connect the automation scripts and EDA workflow
- Execute the complete pipeline from a clean environment
- Generate experiment and metric reports
- Document the design and automation architecture
- Present the final project with reproducible results
Build Practical AI-Driven Chip Engineering Skills
RTL → EDA Automation → PPA Analysis → Machine Learning → Design Exploration
Understand the Design
Learn the digital hardware concepts and constraints behind the design process.
Automate the Workflow
Use Python and Linux tools to execute repeatable engineering tasks.
Integrate Open-Source EDA
Connect synthesis and supported physical-design tools into an automated pipeline.
Measure the Results
Collect implementation metrics and compare design alternatives.
Apply Machine Learning
Train models to analyse data and predict selected design metrics.
Explore AI-Assisted EDA
Use AI techniques to support engineering analysis, automation and optimisation experiments.
Deliver the Project
Produce a reproducible workflow, analysed results and technical documentation.
Course Journey
Semiconductor Fundamentals → AI Chip Architecture → Python → Linux & Git → EDA Fundamentals → Yosys → OpenROAD → RTL-to-GDSII → Python Tool Automation → PPA Analysis → Design-Space Exploration → Data Analysis → Machine Learning → Predictive Modelling → AI-Assisted EDA → Automated Optimisation Experiments → Capstone Project
AI Chip Design & EDA Automation: What the Job Market Is Telling Us
News & Industry Insights
AI Infrastructure Is Creating Demand
Beyond Traditional CPUs Gartner forecasts global semiconductor revenue will reach $1.6 trillion in 2026, driven by AI infrastructure and continued investment in computing hardware. This creates opportunities across the wider chip ecosystem, including accelerators, memory, networking and supporting design technologies.
India Is Building Its Semiconductor Engineering Talent Pipeline
India's Ministry of Electronics and IT reported in July 2026 that more than 68,000 people had been skilled in chip design under semiconductor talent-development initiatives. As India's semiconductor ecosystem expands, practical chip-design, verification and automation skills are increasingly relevant to workforce development.
AI-Assisted EDA Is Appearing in Real Engineering Job Requirements
Cadence has advertised a Hyderabad-based Lead Design Engineer role involving AI-assisted floorplanning, AI-agent-based partitioning, physical-design automation and Python/Tcl scripting. This is a concrete example of AI and automation entering semiconductor design workflows—not just a research topic. The advertised position is experienced-level, so it should not be interpreted as an entry-level vacancy.
Semiconductor Investment Is Extending Across the Supply Chain
TSMC reported record quarterly profit in October 2026 amid strong AI-related demand and maintained its substantial 2026 capital-spending plan. Expanding chip manufacturing and advanced packaging capacity reinforces the importance of engineering across chip design, implementation, validation and production workflows.
Tools & Technologies
Programming & Automation
Python | Bash | Git | GitHub | Jupyter
Open-Source Digital Design & Synthesis
Yosys | Verilog RTL | Netlist Analysis
Physical Design & Implementation
OpenROAD | OpenROAD Flow Scripts | KLayout | Supported Open-Source PDKs
Data Analysis & Machine Learning
NumPy | pandas | Matplotlib | scikit-learn
Development Environment
Linux | Docker | Reproducible Build Environments | Automation Scripts
AI-Assisted Engineering
LLM-Assisted Workflows | Report Analysis | Script Assistance | Validation Pipelines
Chip Design Metrics
Power, Performance and Area (PPA) | Timing Reports | Area Reports | Design-Space Exploration
Who is this Program For?
VLSI & RTL Engineers
Extend existing digital design knowledge into automation, implementation analysis and data-driven experimentation.
ASIC Verification Engineers
Explore Python-based engineering automation, data analysis and EDA workflow integration.
Physical Design & Implementation Engineers
Learn to automate supported implementation runs and compare design metrics systematically.
Python & Software Engineers Entering Semiconductor Engineering
Build the digital design and EDA foundations needed to work on semiconductor automation projects.
AI & Machine Learning Engineers
Explore practical applications of data analysis and ML in chip-design workflows.
Semiconductor Researchers & Engineering Students
Build reproducible experiments around open-source EDA, PPA analysis and AI-assisted optimisation.
Recommended prerequisite
Basic digital logic and familiarity with Verilog or RTL are strongly recommended. Learners without a VLSI background should complete foundational digital design and RTL training before attempting the advanced project modules.
Your Carrer Outcome
Prepare for Emerging Chip Automation & EDA Roles
The programme develops skills relevant to areas such as:
Why Primebit Solution?
Practical Engineering Focus
Connect programming, EDA tools, design metrics and automation through structured laboratory exercises.
Open-Source Toolchain
Learn using accessible tools for digital synthesis, implementation and design analysis.
AI + Semiconductor Integration
Explore where machine learning and AI-assisted workflows can contribute to chip-design engineering.
Automation-First Learning
Build repeatable scripts and pipelines instead of relying only on manual tool execution.
Data-Driven Engineering
Learn to collect, compare and interpret results from design experiments.
Project-Oriented Development
Develop a portfolio project that demonstrates automation, analysis and reproducible engineering practices.
Leading Semiconductor & Technology Companies to Explore
NVIDIA, AMD, Intel, Qualcomm, Arm, Broadcom, MediaTek, Apple, Google, Samsung Semiconductor, Marvell Technology, Synopsys, Cadence Design Systems, Siemens EDA, NXP Semiconductors, Renesas Electronics, Texas Instruments, STMicroelectronics, Infineon Technologies, Microchip Technology.
PROGRAM AT A GLANCE
Duration & Practical Hours
Flexible 6/8 months, 200+ Hours
Mode
Live Hands-on Training
Core Focus
AI Chip Design & Open-Source EDA Automation
Core Focus
Model deployment • Pipeline automation • LLM application development • Production evaluation • Monitoring
Programming
Python & Bash
EDA Tools
Yosys, OpenROAD & KLayout
AI/ML
Data Analysis, Predictive Modelling & AI-Assisted Workflows
Project
Automated EDA Flow & Design-Metric Analysis
Get the detailed course brochure
Enter your details and receive the complete 6/8 months course curriculum, projects and technology stack.
We respect your privacy. Your information is safe with us.
Frequently Asked Questions
The programme is best suited to learners with basic digital logic and some familiarity with RTL. Beginners can enter after completing the relevant VLSI and RTL fundamentals.
Its main focus is AI-assisted EDA automation: using software, ML methods and open-source design tools to automate workflows and analyse chip-design results. It is not a complete course in designing a commercial AI accelerator architecture from scratch.
The practical pathway is designed around open-source tools such as Python, Yosys, OpenROAD, KLayout and selected ML libraries. Specific capabilities depend on the selected tools, versions and supported design platforms.
Cloud and container-based execution can be covered as part of reproducible engineering workflows. Advanced cloud/HPC infrastructure, distributed compute and large-scale EDA deployment would require additional depth and infrastructure beyond the core local-lab exercises.
You can build a baseline ML model using data collected from supported design experiments. The model may predict selected metrics or assist with design exploration. Its accuracy and usefulness must be evaluated against actual tool results.
The core project focuses on automating and analysing a supported digital design flow. Designing a sophisticated AI accelerator requires additional expertise in computer architecture, digital design, memory systems, parallel computing and hardware verification.
The programme can help build relevant foundations and project evidence. Employment depends on your prior skills, the depth of your projects, interview performance and the specific requirements of each role.
BUILD YOUR AI CHIP ENGINEERING SKILLS
Automate. Analyse. Optimise. Innovate
Build practical skills across
Python-Based EDA Automation • Open-Source Chip Design Flows • RTL-to-GDSII Fundamentals • PPA Analysis & Design-Space Exploration • Machine Learning for Engineering Data • AI-Assisted EDA WorkflowsÂ
Explore the intersection of AI, software automation and semiconductor engineering with Primebit Solution.
Primebit Solution — Practical Training for Modern Engineering Careers
- AI Chip Design
- EDA Toolchain
- RTL-to-GDSII
- Python Automation
- PPA Optimization
Design the silicon. Automate the EDA flow. Shape the future of AI hardware..