Lightweight Structural Choices Operator for Technology Mapping

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 li...

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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Grosnit, Antoine, Zimmer, Matthieu, Tutunov, Rasul, Li, Xing, Chen, Lei, Yang, Fan, Yuan, Mingxuan, Bou-Ammar, Haitham
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
Benchmark testing
Complexity theory
Design automation
logic synthesis
Machine learning
structural bias problem
structural choice operators
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Summary: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%).
DOI:10.1109/DAC56929.2023.10247838