Design And Development Of A Residual-Enhanced Gradient Boosting Model For Gate Delay-Based Performance Prediction

Abstract

Thus, Gate delay prediction is the foundation for modern VLSI and digital circuit design. Since the gate dimensions are getting smaller and smaller and gate delay approximation methods are unable to take into account this level technology non-linearities, process variations etc. In this work we exploit residuals to build a Residual-Enhance Gate delay Gradient Boosting Model, which directly leverages the iterative correction of prediction by those able to use map of overall detailed non-linear contribution between features on circuit parameters. For instance, we might append polynomial features to a gradient boosting model and use the residuals of this prediction as a feature in a second-order and new model that learns valid relationships between these residual values to then converge to produce higher accuracy predictions. This strategy takes advantage of ensemble learning benefits while overcoming the downsides of conventional methods and also providing a better treatment to complicated high-dimensional datasets. It has Outperformed the previous models I was using, like Xgboost Random Forest and Lightgbm when it comes to error metrics: MSE, RMSE, MAE experimental results demonstrate the effectiveness of the model in a few different aspects. We show residual correction to greatly improve gate delay estimates, demonstrating its potential for next generation VLSI design problems. So this approach is beneficial in tackling gate delay prediction issues, thus making it a more feasible solution enhancing timing closure and performance of the integrated circuits.