Lightweight Structural Choices Operator for Technology Mapping
Antoine Grosnit, Matthieu Zimmer, Rasul Tutunov, Xing Li, Lei Chen, Fan Yang, Mingxuan Yuan, Haitham Bou-Ammar
摘要
Technology mapping quality heavily depends on the subject graph structure. To overcome structural biases, operators construct choice nodes to enable mappings with improved node and level counts. Nevertheless, state-of-the-art structural choice operators scale poorly with graph size.We present the lightweight structural choices (LCH) operator that incorporates equivalencies by processing only subparts of the graph. We propose multiple heuristics that rely on specific node extraction orders and subpart sizes to extract non-overlapping components. Compared to state-of-the-art methods on EPFL circuits, LCH is 2.35x faster enduring a small sacrifice in node count (3%) and level reduction (2%).
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