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An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning

Zhen Li, Kaixiang Zhu, Xuegong Zhou, Lingli Wang

Abstract

We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.

An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning

Abstract

We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.
Paper Structure (14 sections, 4 figures, 1 table)

This paper contains 14 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries And Related Work
  3. Boolean Logic Optimization
  4. Circuit Partitioning
  5. Reinforcement learning
  6. Prior works
  7. Partitioning Strategy
  8. Adaptive Partitioning
  9. MFFC-Based KaHyPar and DagP
  10. Proposed Logic Optimization Framework
  11. Experimental Results
  12. Experimental Setup
  13. End-to-end Results
  14. Conclusion

Figures (4)

  • Figure 1: PartitionFlow
  • Figure 2: MFFC decomposition
  • Figure 3: Optimize Framework
  • Figure 4: ExperimentFlow