A Cross-Layer Framework for Temporal Power and Supply Noise Prediction

Abstract

In modern microprocessor and SoC designs, supply noise margin has been significantly reduced due to the continuously decreasing supply voltage level. On the other hand, with increasing current density, chips may see larger supply noise variations on various spots and from time to time. As a result, chip robustness and reliability are inevitably deteriorated with more frequent supply noise emergencies. It is therefore crucial to have an efficient supply noise prediction method to enhance design robustness. The state-of-art solutions either try to build a spatial noise estimation framework at the layout-level using the limited distributed physical noise sensors or attempt to develop emergency predictors at the architecture-level thus ignore back-end power delivery details. In this paper, we propose a cross-layer framework for temporal supply noise prediction. Our method not only accounts for the temporal characteristics of workload execution at micro-architecture-level but also incorporates the power delivery model at the circuit-level into such system-level prediction. In order to enable the capability of on-the-fly noise prediction, we first bridge the gap between system-level workload and micro-architectural-level power by employing an ordinary least square-based power estimation model and an adaptive auto-regressive integrated moving average model (ARIMA)-based power prediction model. Then a layout-level supply noise model is developed to explore the correlations between micro-architectural-level power and layout-level supply noise. Compared with existing methods, the proposed ARIMA-based power model improves the prediction performance by up to 37.5%/63.0% in X86/ARM. Moreover, compared with SPICE simulation, our framework is able to estimate present supply noise with an average error of 0.005% and predict future supply noise with an average error of 1.58%/1.17% for X86/ARM architecture.