Using AI for Performance Verification of High-End Processors

Abstract

We present results of performing analytics and visualizations over micro-architectural performance metrics collected in simulation of high-end processor designs. These results contribute to several use-cases: Obtain fast alerts in cases of anomalous behavior of the design, create a global view of performance-related coverage, and compare different versions of the hardware model as an aid to identification of root-causes of performance differences and correlations between metrics. We show case our methods and results through experiments on a very-high-end processor design, and discuss how they are expected to affect the methodology of performance verification of next-generation designs from the vendor.