HDLRegression

HDLRegression is a user-friendly Python3-based regression test runner designed to simplify and accelerate the FPGA verification workflow. HDLRegression is perfect for everything from small FPGA modules to large FPGA projects, and will fit into an existing setup, minimizing both setup time and complexity.

HDLRegression is all about simplicity and efficiency: Quick setup, minimal changes, maximum productivity.

Benefits of using HDLRegression

✅ Fast Integration: Easily adapt your existing verification environment.

✅ Easy Configuration: Replace TCL scripts or Makefiles with simple Python3 scripts.

✅ Efficient Workflow: Run simulations locally or in Continuous Integration (CI) environments.

Getting Started in 3 Easy Steps

Integrating HDLRegression into an existing FPGA verification workflow is straightforward:

📌 Step 1: Prepare Testbench

Mark testbench entities with an HDLRegression comment:

-- hdlregression:tb
entity foo_tb is
end entity;

HDLRegression will work with most verification frameworks.

📌 Step 2: Configure Simulation Script

Set up a simple Python script (e.g. run_sim.py) in the project's directory:

from hdlregression import HDLRegression

hr = HDLRegression()
hr.add_files("src/", "design_lib")   # Path to your design files
hr.add_files("tb/", "tb_lib")        # Path to your testbench files

hr.start()

📌 Step 3: Run Simulations

Start the simulations:

python run_sim.py

HDLRegression compiles, runs, and reports the results automatically.

Documentation

📚 Documentation (PDF, HTML, and RST) can be found in the /doc directory.

Installation

Install HDLRegression using pip:

python -m pip install -e .

Or, manually add HDLRegression in regression script:

import sys
sys.path.append("<path_to_hdlregression_folder>")

Contributing

🤝 HDLRegression is open-source and welcomes contributions. Submit your ideas, bug reports, or improvements via GitHub issues or pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.