MapTune: Advancing ASIC Technology Mapping via Reinforcement Learning Guided Library Tuning

TL;DR

MapTune leverages reinforcement learning to optimize technology mapping, reducing search space and improving circuit performance across diverse benchmarks and technologies.

Contribution

This paper introduces MapTune, a novel reinforcement learning framework that enhances technology mapping by making design-specific cell selection decisions.

Findings

Achieves 22.54% average ADP improvement across benchmarks.

Reduces search space and improves mapping accuracy.

Consistent performance gains across multiple technologies and mappers.

Abstract

Technology mapping involves mapping logical circuits to a library of cells. Traditionally, the full technology library is used, leading to a large search space and potential overhead. Motivated by randomly sampled technology mapping case studies, we propose MapTune framework that addresses this challenge by utilizing reinforcement learning to make design-specific choices during cell selection. By learning from the environment, MapTune refines the cell selection process, resulting in a reduced search space and potentially improved mapping quality. The effectiveness of MapTune is evaluated on a wide range of benchmarks, different technology libraries and technology mappers. The experimental results demonstrate that MapTune achieves higher mapping accuracy and reducing delay/area across diverse circuit designs, technology libraries and mappers. The paper also discusses the Pareto-Optimal exploration and confirms the perpetual delay-area trade-off. Conducted on benchmark suites ISCAS 85/89, ITC/ISCAS 99, VTR8.0 and EPFL benchmarks, the post-technology mapping and post-sizing quality-of-results (QoR) have been significantly improved, with average Area-Delay Product (ADP) improvement of 22.54\% among all different exploration settings in MapTune. The improvements are consistently remained for four different technologies (7nm, 45nm, 130nm, and 180 nm) and two different mappers.