OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach

TL;DR

This paper introduces OPTDTALS, a novel approximate logic synthesis method that uses optimal decision trees to improve circuit approximation quality, balancing complexity and accuracy more effectively than previous heuristics.

Contribution

It presents a new ALS approach utilizing optimal decision trees, providing better control over the trade-off between circuit complexity and accuracy.

Findings

Improved circuit complexity and accuracy over state-of-the-art methods.

Guarantees of optimality enable better trade-offs in approximation.

Experimental results demonstrate clear quality improvements.

Abstract

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.