Stiff Circuit System Modeling via Transformer

TL;DR

This paper introduces a novel modeling approach for stiff circuit systems using a combination of Crossformer, a Transformer-based time-series model, and Kolmogorov-Arnold Networks to improve prediction accuracy and efficiency.

Contribution

It presents a new method integrating Crossformer and KANs for stiff circuit modeling, enhancing prediction fidelity and reducing training time compared to previous methods.

Findings

Achieved higher accuracy in circuit response prediction.

Reduced training time significantly.

Validated on SPICE simulation datasets for ADC circuits.

Abstract

Accurate and efficient circuit behavior modeling is a cornerstone of modern electronic design automation. Among different types of circuits, stiff circuits are challenging to model using previous frameworks. In this work, we propose a new approach using Crossformer, which is a current state-of-the-art Transformer model for time-series prediction tasks, combined with Kolmogorov-Arnold Networks (KANs), to model stiff circuit transient behavior. By leveraging the Crossformer's temporal representation capabilities and the enhanced feature extraction of KANs, our method achieves improved fidelity in predicting circuit responses to a wide range of input conditions. Experimental evaluations on datasets generated through SPICE simulations of analog-to-digital converter (ADC) circuits demonstrate the effectiveness of our approach, with significant reductions in training time and error rates.