aigverse: A Unified Infrastructure for Machine Learning-Driven Logic Synthesis

The rapid advancement of Machine Learning (ML) has demonstrated immense potential for logic synthesis, design-space exploration, and circuit optimization. In practice, however, researchers who want to combine these areas repeatedly face the same infrastructure problem: the surrounding ML and data-science ecosystem is centered on Python, whereas the core data structures and algorithms of logic synthesis are the product of decades of highly optimized C/C++ engineering. As a result, individual projects are often forced either to rebuild logic synthesis functionality from scratch in Python or to assemble brittle wrappers and file-based conversion pipelines around external tools. To address this recurring gap, this paper introduces aigverse, a unified open-source infrastructure project that brings mature logic synthesis capabilities into Python-first workflows without reimplementing them there. To this end, aigverse wraps high-performance C/C++ synthesis backends within an idiomatic Python interface and provides reusable support for circuit construction and manipulation, dataset generation, optimization and equivalence-checking flows, and export into graph and tensor representations for downstream data science and ML pipelines. In a case study comparing a pure-Python tensorization pipeline from the literature against aigverse, the latter achieves up to 17.2× faster end-to-end tensorization while reducing isolated framework hand-off overhead by three to four orders of magnitude depending on circuit size. In this way, aigverse is intended to provide for logic synthesis the kind of reusable, domain-aware software bridge that has already helped ML become effective in scientific fields such as medicine and chemistry without requiring those researchers to become application-domain experts.