OpenLS-DGF generates and packages a 966k-circuit multi-task logic synthesis dataset from 46 designs, demonstrated on four ML tasks with high reported accuracy.
Machine Learning for Electronic Design Automation: A Survey
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abstract
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.
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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis
OpenLS-DGF generates and packages a 966k-circuit multi-task logic synthesis dataset from 46 designs, demonstrated on four ML tasks with high reported accuracy.