The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which is impractical for expensive to simulate circuits. We propose a learning-based algorithm that can be trained using a small amount of data and, therefore, scalable to tasks with expensive simulations. Our efficient algorithm solves the post-layout performance optimization problem where simulations are known to be expensive. Our comprehensive study also solves the schematic-level sizing problem. For efficient optimization, we utilize Bayesian Neural Networks as a regression model to approximate circuit performance. For layout-aware optimization, we handle the problem as a multi-fidelity optimization problem and improve efficiency by exploiting the correlations from cheaper evaluations. We present three test cases to demonstrate the efficiency of our algorithms. Our tests prove that the proposed approach is more efficient than conventional baselines and state-of-the-art algorithms.

翻译：高昂的仿真成本一直是模拟/混合信号设计自动化的实际瓶颈。许多基于学习的算法需要数千个仿真数据点，这对于仿真成本高昂的电路而言并不实用。我们提出了一种基于学习的算法，该算法仅需少量数据即可训练，因此可扩展至仿真代价昂贵的任务。所提高效算法解决了仿真成本已知高昂的后版图性能优化问题，同时我们全面的研究也解决了原理图级尺寸优化问题。为实现高效优化，我们采用贝叶斯神经网络作为回归模型来近似电路性能。在布局感知优化中，我们将该问题视为多保真度优化问题，并通过利用廉价评估数据间的相关性来提升效率。我们通过三个测试案例验证了算法的有效性。实验证明，所提方法比传统基线方法和现有最新算法效率更高。