Event Prediction in Processors Using Deep Temporal Models

Abstract

In order to achieve high processing efficiencies, next generation computer architecture designs need an effective Artificial Intelligence (AI)-framework to learn large-scale processor interactions. In this short paper, we present Deep Temporal Models (DTMs) that offer effective and scalable time-series representations to addresses key challenges for learning processor data: high data rate, cyclic patterns, and high dimensionality. We present our approach using DTMs to learn and predict processor events. We show comparisons using these learning models with promising initial simulation results.