Efficient Continuous Logic Optimization with Diffusion Model

The logic synthesis optimization flow is crucial to the quality of results (QoR), which applies a sequence of transformations to a design. Recently, there has been a growing focus on the automatic optimization of synthesis flows to improve QoR, utilizing techniques such as Bayesian optimization and reinforcement learning, which may fall short in efficiency due to the exponentially large search space. In contrast, continuous optimization offers notable efficiency advantages by leveraging the explicit gradient. However, despite its potential, several significant concerns remain to be addressed. On one hand, it is essential to obtain a reliable gradient. On the other hand, a major challenge arises from the fact that searching within a continuous space can yield solutions that deviate from feasible ones. In this paper, we propose an efficient approach to optimize synthesis sequences within a continuous latent space. Specifically, the gradient information is derived from a QoR surrogate model, while the discrepancies between solutions and feasible transformations are minimized by a diffusion model. Experimental results on extensive benchmarks demonstrate that the proposed method not only achieves lower area and delay but also improves efficiency by 5 X to 130 X, compared with previous methods.