| Name | aigverse JSON |
| Version |
0.0.11
JSON |
| download |
| home_page | https://github.com/marcelwa/aigverse |
| Summary | A Python library for working with logic networks, synthesis, and optimization. |
| upload_time | 2024-11-07 20:21:03 |
| maintainer | None |
| docs_url | None |
| author | Marcel Walter |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Marcel Walter Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
aigverse
logic
synthesis
aig
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# aigverse: A Python Library for Logic Networks, Synthesis, and Optimization
[](https://github.com/marcelwa/aigverse/actions/workflows/aigverse-pypi-deployment.yml)
[](https://github.com/marcelwa/aigverse/blob/main/LICENSE)
[](https://pypi.org/project/aigverse/)
[](https://github.com/marcelwa/aigverse/releases)
> [!Important]
> This project is still in the early stages of development. The API is subject to change, and some features may not be
> fully implemented. I appreciate your patience and understanding as work to improve the library continues.
`aigverse` is a Python framework designed to bridge the gap between logic synthesis and AI/ML applications. It allows
you to represent and manipulate logic circuits efficiently, making it easier to integrate logic synthesis tasks into
machine learning pipelines. By leveraging the
powerful [EPFL Logic Synthesis Libraries](https://arxiv.org/abs/1805.05121),
particularly [mockturtle](https://github.com/lsils/mockturtle), `aigverse` provides a high-level Python interface to
state-of-the-art algorithms for And-Inverter Graph (AIG) manipulation and logic synthesis, widely used in formal
verification, hardware design, and optimization tasks.
## Features
- **Efficient Logic Representation**: Use And-Inverter Graphs (AIGs) to model and manipulate logic circuits in Python.
- **File Format Support**: Read and write AIGER, Verilog, Bench, PLA, ... files for interoperability with other logic
synthesis tools.
- **C++ Backend**: Leverage the performance of the EPFL Logic Synthesis Libraries for fast logic synthesis and
optimization.
- **High-Level API**: Simplify logic synthesis tasks with a Pythonic interface for AIG manipulation and optimization.
- **Integration with Machine Learning**: Convenient integration with popular data science libraries.
## Motivation
As AI and machine learning (ML) increasingly impact hardware design automation, there's a growing need for tools that
integrate logic synthesis with ML workflows. `aigverse` provides a Python-friendly interface for logic synthesis, making
it easier to develop applications that blend both AI/ML and traditional circuit synthesis techniques. With `aigverse`,
you can parse, manipulate, and optimize logic circuits directly from Python. Eventually, we aim to provide seamless
integration with popular ML libraries, enabling the development of novel AI-driven synthesis and optimization tools.
## Installation
`aigverse` requires Python 3.8+ and is built using the EPFL Logic Synthesis Libraries
with [pybind11](https://github.com/pybind/pybind11). To install `aigverse`:
```bash
pip install aigverse
```
## Usage
### Basic Example: Creating an AIG
In `aigverse`, you can create a simple And-Inverter Graph (AIG) and manipulate it using various logic operations.
```python
from aigverse import Aig
# Create a new AIG network
aig = Aig()
# Create primary inputs
x1 = aig.create_pi()
x2 = aig.create_pi()
# Create logic gates
f_and = aig.create_and(x1, x2) # AND gate
f_or = aig.create_or(x1, x2) # OR gate
# Create primary outputs
aig.create_po(f_and)
aig.create_po(f_or)
# Print the size of the AIG network
print(f"AIG Size: {aig.size()}")
```
### Iterating over AIG Nodes
You can iterate over all nodes in the AIG, or specific subsets like the primary inputs or only logic nodes (gates).
```python
# Iterate over all nodes in the AIG
for node in aig.nodes():
print(f"Node: {node}")
# Iterate only over primary inputs
for pi in aig.pis():
print(f"Primary Input: {pi}")
# Iterate only over logic nodes (gates)
for gate in aig.gates():
print(f"Gate: {gate}")
# Iterate over the fanins of a node
n_and = aig.get_node(f_and)
for fanin in aig.fanins(n_and):
print(f"Fanin of {n_and}: {fanin}")
```
### Depth and Level Computation
You can compute the depth of the AIG network and the level of each node. Depth information is useful for estimating the
critical path delay of a respective circuit.
```python
from aigverse import DepthAig
depth_aig = DepthAig(aig)
print(f"Depth: {depth_aig.num_levels()}")
for node in aig.nodes():
print(f"Level of {node}: {depth_aig.level(node)}")
```
### Logic Optimization
You can optimize AIGs using various algorithms. For example, you can perform resubstitution to simplify logic using
shared divisors. Similarly, refactoring collapses maximmal fanout-free cones (MFFCs) into truth tables and resynthesizes
them into new structures. Cut rewriting optimizes the AIG by replacing cuts with improved ones from a pre-computed NPN
database.
```python
from aigverse import aig_resubstitution, sop_refactoring, aig_cut_rewriting
# Clone the AIG network for size comparison
aig_clone = aig.clone()
# Optimize the AIG with several optimization algorithms
for optimization in [aig_resubstitution, sop_refactoring, aig_cut_rewriting]:
optimization(aig)
# Print the size of the unoptimized and optimized AIGs
print(f"Original AIG Size: {aig_clone.size()}")
print(f"Optimized AIG Size: {aig.size()}")
```
### Equivalence Checking
Equivalence of AIGs (e.g., after optimization) can be checked using SAT-based equivalence checking.
```python
from aigverse import equivalence_checking
# Perform equivalence checking
equiv = equivalence_checking(aig1, aig2)
if equiv:
print("AIGs are equivalent!")
else:
print("AIGs are NOT equivalent!")
```
### AIGER Files
You can read and write (ASCII) [AIGER](https://fmv.jku.at/aiger/) files.
#### Parsing
```python
from aigverse import read_aiger_into_aig, read_ascii_aiger_into_aig
# Read AIGER files into AIG networks
aig1 = read_aiger_into_aig("example.aig")
aig2 = read_ascii_aiger_into_aig("example.aag")
# Print the size of the AIGs
print(f"AIG Size: {aig1.size()}")
print(f"AIG Size: {aig2.size()}")
```
#### Writing
```python
from aigverse import write_aiger
# Write an AIG network to an AIGER file
write_aiger(aig, "example.aig")
```
### Exporting Edge Lists
You can export the AIG as an edge list, which is useful for integration with graph libraries like NetworkX.
```python
from aigverse import to_edge_list
# Export the AIG as an edge list
edges = to_edge_list(aig)
print(edges)
# Convert to list of tuples
edges = [(e.source, e.target, e.weight) for e in edges]
```
### Truth Tables
Small Boolean functions can be efficiently represented using truth tables. `aigverse` enables the creation and
manipulation of truth tables by wrapping a portion of the [kitty](https://github.com/msoeken/kitty) library.
#### Creation
```python
from aigverse import TruthTable
# Initialize a truth table with 3 variables
tt = TruthTable(3)
# Create a truth table from a hex string representing the MAJ function
tt.create_from_hex_string("e8")
```
#### Manipulation
```python
# Flip each bit in the truth table
for i in range(tt.num_bits()):
print(f"Flipping bit {int(tt.get_bit(i))}")
tt.flip_bit(i)
# Print a binary string representation of the truth table
print(tt.to_binary())
# Clear the truth table
tt.clear()
# Check if the truth table is constant 0
print(tt.is_const0())
```
#### Symbolic Simulation of AIGs
```python
from aigverse import simulate
# Obtain the truth table of each AIG output
tts = simulate(aig)
# Print the truth tables
for i, tt in enumerate(tts):
print(f"PO{i}: {tt.to_binary()}")
```
#### Exporting as Lists of Lists
For some machine learning applications, it may be useful to export the truth table as a list of lists.
```python
# Export the truth table as a list of lists
tt_list = [[int(tt.get_bit(i)) for i in range(tt.num_bits())] for tt in tts]
```
## Contributing
Contributions are welcome! If you'd like to contribute to `aigverse`, please submit a pull request or open an issue. I
appreciate feedback and suggestions for improving the library.
## License
`aigverse` is available under the MIT License.
Raw data
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"description": "# aigverse: A Python Library for Logic Networks, Synthesis, and Optimization\n\n[](https://github.com/marcelwa/aigverse/actions/workflows/aigverse-pypi-deployment.yml)\n[](https://github.com/marcelwa/aigverse/blob/main/LICENSE)\n[](https://pypi.org/project/aigverse/)\n[](https://github.com/marcelwa/aigverse/releases)\n\n> [!Important]\n> This project is still in the early stages of development. The API is subject to change, and some features may not be\n> fully implemented. I appreciate your patience and understanding as work to improve the library continues.\n\n`aigverse` is a Python framework designed to bridge the gap between logic synthesis and AI/ML applications. It allows\nyou to represent and manipulate logic circuits efficiently, making it easier to integrate logic synthesis tasks into\nmachine learning pipelines. By leveraging the\npowerful [EPFL Logic Synthesis Libraries](https://arxiv.org/abs/1805.05121),\nparticularly [mockturtle](https://github.com/lsils/mockturtle), `aigverse` provides a high-level Python interface to\nstate-of-the-art algorithms for And-Inverter Graph (AIG) manipulation and logic synthesis, widely used in formal\nverification, hardware design, and optimization tasks.\n\n## Features\n\n- **Efficient Logic Representation**: Use And-Inverter Graphs (AIGs) to model and manipulate logic circuits in Python.\n- **File Format Support**: Read and write AIGER, Verilog, Bench, PLA, ... files for interoperability with other logic\n synthesis tools.\n- **C++ Backend**: Leverage the performance of the EPFL Logic Synthesis Libraries for fast logic synthesis and\n optimization.\n- **High-Level API**: Simplify logic synthesis tasks with a Pythonic interface for AIG manipulation and optimization.\n- **Integration with Machine Learning**: Convenient integration with popular data science libraries.\n\n## Motivation\n\nAs AI and machine learning (ML) increasingly impact hardware design automation, there's a growing need for tools that\nintegrate logic synthesis with ML workflows. `aigverse` provides a Python-friendly interface for logic synthesis, making\nit easier to develop applications that blend both AI/ML and traditional circuit synthesis techniques. With `aigverse`,\nyou can parse, manipulate, and optimize logic circuits directly from Python. Eventually, we aim to provide seamless\nintegration with popular ML libraries, enabling the development of novel AI-driven synthesis and optimization tools.\n\n## Installation\n\n`aigverse` requires Python 3.8+ and is built using the EPFL Logic Synthesis Libraries\nwith [pybind11](https://github.com/pybind/pybind11). To install `aigverse`:\n\n```bash\npip install aigverse\n```\n\n## Usage\n\n### Basic Example: Creating an AIG\n\nIn `aigverse`, you can create a simple And-Inverter Graph (AIG) and manipulate it using various logic operations.\n\n```python\nfrom aigverse import Aig\n\n# Create a new AIG network\naig = Aig()\n\n# Create primary inputs\nx1 = aig.create_pi()\nx2 = aig.create_pi()\n\n# Create logic gates\nf_and = aig.create_and(x1, x2) # AND gate\nf_or = aig.create_or(x1, x2) # OR gate\n\n# Create primary outputs\naig.create_po(f_and)\naig.create_po(f_or)\n\n# Print the size of the AIG network\nprint(f\"AIG Size: {aig.size()}\")\n```\n\n### Iterating over AIG Nodes\n\nYou can iterate over all nodes in the AIG, or specific subsets like the primary inputs or only logic nodes (gates).\n\n```python\n# Iterate over all nodes in the AIG\nfor node in aig.nodes():\n print(f\"Node: {node}\")\n\n# Iterate only over primary inputs\nfor pi in aig.pis():\n print(f\"Primary Input: {pi}\")\n\n# Iterate only over logic nodes (gates)\nfor gate in aig.gates():\n print(f\"Gate: {gate}\")\n\n# Iterate over the fanins of a node\nn_and = aig.get_node(f_and)\nfor fanin in aig.fanins(n_and):\n print(f\"Fanin of {n_and}: {fanin}\")\n```\n\n### Depth and Level Computation\n\nYou can compute the depth of the AIG network and the level of each node. Depth information is useful for estimating the\ncritical path delay of a respective circuit.\n\n```python\nfrom aigverse import DepthAig\n\ndepth_aig = DepthAig(aig)\nprint(f\"Depth: {depth_aig.num_levels()}\")\nfor node in aig.nodes():\n print(f\"Level of {node}: {depth_aig.level(node)}\")\n```\n\n### Logic Optimization\n\nYou can optimize AIGs using various algorithms. For example, you can perform resubstitution to simplify logic using\nshared divisors. Similarly, refactoring collapses maximmal fanout-free cones (MFFCs) into truth tables and resynthesizes\nthem into new structures. Cut rewriting optimizes the AIG by replacing cuts with improved ones from a pre-computed NPN\ndatabase.\n\n```python\nfrom aigverse import aig_resubstitution, sop_refactoring, aig_cut_rewriting\n\n# Clone the AIG network for size comparison\naig_clone = aig.clone()\n\n# Optimize the AIG with several optimization algorithms\nfor optimization in [aig_resubstitution, sop_refactoring, aig_cut_rewriting]:\n optimization(aig)\n\n# Print the size of the unoptimized and optimized AIGs\nprint(f\"Original AIG Size: {aig_clone.size()}\")\nprint(f\"Optimized AIG Size: {aig.size()}\")\n```\n\n### Equivalence Checking\n\nEquivalence of AIGs (e.g., after optimization) can be checked using SAT-based equivalence checking.\n\n```python\nfrom aigverse import equivalence_checking\n\n# Perform equivalence checking\nequiv = equivalence_checking(aig1, aig2)\n\nif equiv:\n print(\"AIGs are equivalent!\")\nelse:\n print(\"AIGs are NOT equivalent!\")\n```\n\n### AIGER Files\n\nYou can read and write (ASCII) [AIGER](https://fmv.jku.at/aiger/) files.\n\n#### Parsing\n\n```python\nfrom aigverse import read_aiger_into_aig, read_ascii_aiger_into_aig\n\n# Read AIGER files into AIG networks\naig1 = read_aiger_into_aig(\"example.aig\")\naig2 = read_ascii_aiger_into_aig(\"example.aag\")\n\n# Print the size of the AIGs\nprint(f\"AIG Size: {aig1.size()}\")\nprint(f\"AIG Size: {aig2.size()}\")\n```\n\n#### Writing\n\n```python\nfrom aigverse import write_aiger\n\n# Write an AIG network to an AIGER file\nwrite_aiger(aig, \"example.aig\")\n```\n\n### Exporting Edge Lists\n\nYou can export the AIG as an edge list, which is useful for integration with graph libraries like NetworkX.\n\n```python\nfrom aigverse import to_edge_list\n\n# Export the AIG as an edge list\nedges = to_edge_list(aig)\nprint(edges)\n\n# Convert to list of tuples\nedges = [(e.source, e.target, e.weight) for e in edges]\n```\n\n### Truth Tables\n\nSmall Boolean functions can be efficiently represented using truth tables. `aigverse` enables the creation and\nmanipulation of truth tables by wrapping a portion of the [kitty](https://github.com/msoeken/kitty) library.\n\n#### Creation\n\n```python\nfrom aigverse import TruthTable\n\n# Initialize a truth table with 3 variables\ntt = TruthTable(3)\n# Create a truth table from a hex string representing the MAJ function\ntt.create_from_hex_string(\"e8\")\n```\n\n#### Manipulation\n\n```python\n# Flip each bit in the truth table\nfor i in range(tt.num_bits()):\n print(f\"Flipping bit {int(tt.get_bit(i))}\")\n tt.flip_bit(i)\n\n# Print a binary string representation of the truth table\nprint(tt.to_binary())\n\n# Clear the truth table\ntt.clear()\n\n# Check if the truth table is constant 0\nprint(tt.is_const0())\n```\n\n#### Symbolic Simulation of AIGs\n\n```python\nfrom aigverse import simulate\n\n# Obtain the truth table of each AIG output\ntts = simulate(aig)\n\n# Print the truth tables\nfor i, tt in enumerate(tts):\n print(f\"PO{i}: {tt.to_binary()}\")\n```\n\n#### Exporting as Lists of Lists\n\nFor some machine learning applications, it may be useful to export the truth table as a list of lists.\n\n```python\n# Export the truth table as a list of lists\ntt_list = [[int(tt.get_bit(i)) for i in range(tt.num_bits())] for tt in tts]\n```\n\n## Contributing\n\nContributions are welcome! If you'd like to contribute to `aigverse`, please submit a pull request or open an issue. I\nappreciate feedback and suggestions for improving the library.\n\n## License\n\n`aigverse` is available under the MIT License.\n",
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