Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits

TL;DR

This paper introduces an active sampling method that combines sensitivity analysis and Bayesian surrogate modeling to efficiently explore the design space of analog circuits with many parameters, outperforming traditional Monte-Carlo methods.

Contribution

It presents a novel active sampling flow that integrates sensitivity analysis with Bayesian modeling for improved simulation of analog circuits.

Findings

Outperforms Monte-Carlo sampling in synthetic and real datasets.

Effectively reduces dimensionality in complex analog circuit simulations.

Enhances exploration efficiency of the design space.

Abstract

We propose an active sampling flow, with the use-case of simulating the impact of combined variations on analog circuits. In such a context, given the large number of parameters, it is difficult to fit a surrogate model and to efficiently explore the space of design features. By combining a drastic dimension reduction using sensitivity analysis and Bayesian surrogate modeling, we obtain a flexible active sampling flow. On synthetic and real datasets, this flow outperforms the usual Monte-Carlo sampling which often forms the foundation of design space exploration.