BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation

Abstract

One of the greatest challenges in integrated circuit design is the repeated executions of computationally expensive SPICE simulations, particularly when highly complex chip testing/verification is involved. Recently, pseudo-transient analysis (PTA) has shown to be one of the most promising continuation SPICE solvers. However, the PTA efficiency is highly influenced by the inserted pseudo-parameters. In this work, we proposed BoA-PTA, a Bayesian optimization accelerated PTA that can substantially accelerate simulations and improve convergence performance without introducing extra errors. Furthermore, our method does not require any pre-computation data or offline training. The acceleration framework can either speed up ongoing, repeated simulations (e.g., Monte-Carlo simulations) immediately or improve new simulations of completely different circuits. BoA-PTA is equipped with cutting-edge machine learning techniques, such as deep learning, Gaussian process, Bayesian optimization, non-stationary monotonic transformation, and variational inference via reparameterization. We assess BoA-PTA in 43 benchmark circuits and real industrial circuits against other SOTA methods and demonstrate an average of 1.5x (maximum 3.5x) for the benchmark circuits and up to 250x speedup for the industrial circuit designs over the original CEPTA without sacrificing any accuracy.