An information-theoretic framework for optimal temperature sensor allocation and full-chip thermal monitoring

TLDR

Full‑chip thermal monitoring is a critical and challenging problem in modern microprocessor design. The paper proposes an information‑theoretic framework that models on‑chip temperature variation uncertainty using differential entropy. An optimization scheme based on the framework identifies optimal sensor placements, and efficient numerical algorithms reduce the computational cost of entropy calculation and optimization. Experimental results show the entropy‑based method reduces error by 1.4×, outperforming prior approaches.

Abstract

Full-chip thermal monitoring is an important and challenging issue in today's microprocessor design. In this paper, we propose a new information-theoretic framework to quantitatively model the uncertainty of on-chip temperature variation by differential entropy. Based on this framework, an efficient optimization scheme is developed to find the optimal spatial locations for temperature sensors such that the full-chip thermal map can be accurately captured with a minimum number of on-chip sensors. In addition, several efficient numerical algorithms are proposed to minimize the computational cost of the proposed entropy calculation and optimization. As will be demonstrated by our experimental examples, the proposed entropy-based method achieves superior accuracy (1.4x error reduction) for full-chip thermal monitoring over prior art.