Improving Classification Accuracy of a Machine Learning Approach for FPGA Timing Closure

Abstract

We can use Cloud Computing and Machine Learning to help deliver timing closure of FPGA designs using InTime [2], [3]. This approach requires no modification to the input RTL and relies exclusively on manipulating the CAD tool parameters that drive the optimization heuristics. By running multiple combinations of the parameters in parallel, we learn from results and identify which parameters caused an improvement in the final results. By systematically building a classification model and training it with the results of the parallel CAD runs, we can build an accurate estimation flow for helping identify which parameters are more likely to improve the timing. In this paper, we consider strategies for improving the predictive accuracy of our classifier models to help guide the CAD run towards timing convergence. With ensemble learning we are able to increase average AUC score from 0.74 to 0.79, which could also translate into 2.7× savings in machine learning effort.