Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits
This addresses the challenge of design space exploration in analog circuit simulation, which is incremental as it builds on existing sampling and modeling techniques.
The paper tackled the problem of efficiently simulating the impact of combined variations on analog circuits by proposing an active sampling flow that combines sensitivity analysis and Bayesian surrogate modeling, outperforming Monte-Carlo sampling on synthetic and real datasets.
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.