OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach
This work addresses the problem of reducing circuit complexity with controlled accuracy for circuit designers, representing an incremental improvement over existing heuristic ALS methods.
The paper tackles the problem of Approximate Logic Synthesis (ALS) in circuit design by proposing a methodology that uses optimal decision trees to approximate circuits, achieving a more controllable trade-off between complexity and accuracy. Experimental results show clear improvements in circuit complexity and accuracy compared to state-of-the-art methods.
The growing interest in Explainable Artificial Intelligence (XAI) motivates promising studies of computing optimal Interpretable Machine Learning models, especially decision trees. Such models generally provide optimality in compact size or empirical accuracy. Recent works focus on improving efficiency due to the natural scalability issue. The application of such models to practical problems is quite limited. As an emerging problem in circuit design, Approximate Logic Synthesis (ALS) aims to reduce circuit complexity by sacrificing correctness. Recently, multiple heuristic machine learning methods have been applied in ALS, which learns approximated circuits from samples of input-output pairs. In this paper, we propose a new ALS methodology realizing the approximation via learning optimal decision trees in empirical accuracy. Compared to previous heuristic ALS methods, the guarantee of optimality achieves a more controllable trade-off between circuit complexity and accuracy. Experimental results show clear improvements in our methodology in the quality of approximated designs (circuit complexity and accuracy) compared to the state-of-the-art approaches.