  Reasoning high-level abstractions from bit-blasted Boolean networks (BNs)
such as gate-level netlists can significantly benefit functional verification,
logic minimization, datapath synthesis, malicious logic identification, etc.
Mostly, conventional reasoning approaches leverage structural hashing and
functional propagation, suffering from limited scalability and inefficient
usage of modern computing power. In response, we propose a novel symbolic
reasoning framework exploiting graph neural networks (GNNs) and GPU
acceleration to reason high-level functional blocks from gate-level netlists,
namely Gamora, which offers high reasoning performance w.r.t exact reasoning
algorithms, strong scalability to BNs with over 33 million nodes, and
generalization capability from simple to complex designs. To further
demonstrate the capability of Gamora, we also evaluate its reasoning
performance after various technology mapping options, since
technology-dependent optimizations are known to make functional reasoning much
more challenging. Experimental results show that (1) Gamora reaches almost 100%
and over 97% reasoning accuracy for carry-save-array (CSA) and Booth-encoded
multipliers, respectively, with up to six orders of magnitude speedups compared
to the state-of-the-art implementation in the ABC framework; (2) Gamora
maintains high reasoning accuracy (>92%) in finding functional modules after
complex technology mapping, upon which we comprehensively analyze the impacts
on Gamora reasoning from technology mapping.
