Stiff Circuit System Modeling via Transformer
This work addresses a challenging modeling problem for electronic design automation, but it appears incremental as it combines existing methods (Crossformer and KANs) for a specific domain.
The paper tackles the problem of modeling stiff circuit transient behavior by proposing a new approach using Crossformer and Kolmogorov-Arnold Networks (KANs), achieving significant reductions in training time and error rates in experiments on ADC circuit datasets.
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.