Learning Boolean Circuits from Examples for Approximate Logic Synthesis

Many computing applications are inherently error resilient. Thus, it is possible to decrease computing accuracy to achieve greater efficiency in area, performance, and/or energy consumption. In recent years, a slew of automatic techniques for approximate computing has been proposed; however, most of these techniques require full knowledge of an exact, or ‘golden’ circuit description. In contrast, there has been significant recent interest in synthesizing computation from examples, a form of supervised learning. In this paper, we explore the relationship between supervised learning of Boolean circuits and existing work on synthesizing incompletely-specified functions. We show that when considered through a machine learning lens, the latter work provides a good training accuracy but poor test accuracy. We contrast this with prior work from the 1990s which uses mutual information to steer the search process, aiming for good generalization. By combining this early work with a recent approach to learning logic functions, we are able to achieve a scalable and efficient machine learning approach for Boolean circuits in terms of area/delay/test-error trade-off.