aigverse: A Python Library for Logic Networks, Synthesis, and Optimization

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. aigverse is built directly upon the powerful EPFL Logic Synthesis Libraries, particularly mockturtle, providing 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. By building upon the robust foundation of the EPFL Logic Synthesis Libraries, aigverse delivers powerful logic manipulation capabilities while maintaining accessibility through its Python interface. 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 is built using the EPFL Logic Synthesis Libraries with pybind11. It is available via PyPI for all major operating systems and supports Python 3.9 to 3.13.

pip install aigverse

Usage

The following gives a shallow overview on aigverse. Detailed documentation and examples are available at ReadTheDocs.

Basic Example: Creating an AIG

In aigverse, you can create a simple And-Inverter Graph (AIG) and manipulate it using various logic operations.

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()}")

Note that all primary inputs (PIs) must be created before any logic gates.

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).

# 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.

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)}")

Sequential AIGs

aigverse also supports sequential AIGs, which are AIGs with registers.

from aigverse import SequentialAig

seq_aig = SequentialAig()
x1 = seq_aig.create_pi()  # Regular PI
x2 = seq_aig.create_ro()  # Register output (sequential PI)

f_and = seq_aig.create_and(x1, x2)  # AND gate

seq_aig.create_ri(f_and)  # Register input (sequential PO)

print(seq_aig.registers())  # Prints the association of registers

It is to be noted that the construction of sequential AIGs comes with some caveats:

All register outputs (ROs) must be created after all primary inputs (PIs).
All register inputs (RIs) must be created after all primary outputs (POs).
As for regular AIGs, all PIs and ROs must be created before any logic gates.

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.

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.

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 files.

Parsing

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()}")

Additionally, you can read AIGER files into sequential AIGs using read_aiger_into_sequential_aig and read_ascii_aiger_into_sequential_aig.

Writing

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.

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]

Edge lists also support sequential AIGs. They will have additional connections from register inputs (RIs) to register outputs (ROs) which form feedback loops.

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 library.

Creation

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

# 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

from aigverse import simulate, simulate_nodes

# 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()}")

# Obtain the truth tables of each node in the AIG
n_to_tt = simulate_nodes(aig)

# Print the truth tables of each node
for node, tt in n_to_tt.items():
    print(f"Node {node}: {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.

# 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.

License

wjrforcyber/aigverse

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