Eagle-eye: a near-optimal statistical framework for noise sensor placement

Abstract

The relentless technology scaling has led to significantly reduced noise margin and complicated functionalities. As such, design time techniques per se are less likely to ensure power integrity, resulting in runtime voltage emergencies. To alleviate the issue, recently several works have shed light on the possibilities of dynamic noise management systems. Most of these works rely on on-chip noise sensors to accurately capture voltage emergencies. However, they all assume, either implicitly or explicitly, that the placement of the sensors is given. It remains an open problem in the literature how to optimally place a given number of noise sensors for best voltage emergency detection. In this paper, we formally define the problem of noise sensor placement along with a novel sensing quality metric (SQM) to be maximized. We then put forward an efficient algorithm to solve it, which is proved to be optimal in the class of polynomial complexity approximations. Experimental results on a set of industrial power grid designs show that compared with a simple average-noise based heuristic and two state-of-the-art temperature sensor placement algorithms aiming at recovering the full map or capturing the hot spots at all times, the proposed method on average can reduce the miss rate of voltage emergency detections by 7.4x, 15x and 6.2x, respectively.