Revisit Choice Network for Synthesis and Technology Mapping

Chen Chen, Jiaqi Yin, CUNXI YU

Abstract:

Choice network construction is a critical technique for alleviating structural bias issues in Boolean optimization, equivalence checking,
and technology mapping. Previous works on lossless synthesis utilize independent optimization to generate multiple snapshots, and use simulation
and SAT solvers to identify functionally equivalent nodes. These nodes
are then merged into a subject graph with choice nodes. However, such
methods often neglect the quality of these choices—raising the question of
whether they truly contribute to effective technology mapping. This paper
introduces CRISTAL, a novel methodology and framework to constructing
Boolean choice networks. Specifically, CRISTAL introduces a novel flow of
choice network-based synthesis and mapping, includes representative logic
cone search, structural mutation for generating diverse choice structures
via equality saturation, and priority-ranking choice selection along with
choice network construction and validation. Through these techniques,
CRISTAL constructs fewer but higher-quality choices. Our experimental
results demonstrate that CRISTAL outperforms the state-of-the-art Boolean
choice network construction implemented in ABC in the post-mapping
stage, achieving average reductions of 3.85%/8.35% (area/delay) in delayoriented mode, 0.11%/2.74% in area-oriented mode, and a 63.77% runtime
reduction on large-scale cases, across a diverse set of combinational circuits
from the IWLS 2005, ISCAS’89, and EPFL benchmark suites.