A python module for parsing, processing, and simulating gate-level circuits.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
Stefan Holst 3497bfdc75 first gpu-code, cached test fixtures 2 years ago
docs year bump 4 years ago
src/kyupy first gpu-code, cached test fixtures 2 years ago
tests first gpu-code, cached test fixtures 2 years ago
.gitignore start documentation 4 years ago
Demo.ipynb interface -> io_nodes, io_loc fix 2 years ago
LICENSE.txt year bump 2 years ago
MANIFEST.in move sources to src, add package info and release 0.0.1 4 years ago
README.rst Migration to new logic value representation 4 years ago
setup.py fix tests, version bump 3 years ago

README.rst

KyuPy - Pythonic Processing of VLSI Circuits
============================================

KyuPy is a Python package for processing and analysis of non-hierarchical gate-level VLSI designs.
It contains fundamental building blocks for research software in the fields of VLSI test, diagnosis and reliability:

* Efficient data structures for gate-level circuits and related design data.
* Partial `lark <https://github.com/lark-parser/lark>`_ parsers for common design files like
bench, gate-level verilog, standard delay format (SDF), standard test interface language (STIL).
* Bit-parallel gate-level 2-, 4-, and 8-valued logic simulation.
* GPU-accelerated high-throughput gate-level timing simulation.
* High-performance through the use of `numpy <https://numpy.org>`_ and `numba <https://numba.pydata.org>`_.


Getting Started
---------------

KyuPy is available in `PyPI <https://pypi.org/project/kyupy>`_.
It requires Python 3.6 or newer, `lark-parser <https://pypi.org/project/lark-parser>`_, and `numpy`_.
Although optional, `numba`_ should be installed for best performance.
GPU/CUDA support in numba may `require some additional setup <https://numba.pydata.org/numba-doc/latest/cuda/index.html>`_.
If numba is not available, KyuPy will automatically fall back to slow, pure Python execution.

The Jupyter Notebook `Demo.ipynb <https://github.com/s-holst/kyupy/blob/main/Demo.ipynb>`_ contains some useful examples to get familiar with the API.

To work with the latest pre-release source code, clone the `KyuPy GitHub repository <https://github.com/s-holst/kyupy>`_.
Run ``pip3 install --user -e .`` within your local checkout to make the package available in your Python environment.
The source code comes with tests that can be run with ``pytest``.