Research of thermal sensor allocation and placement based on dual clustering for microprocessors

Abstract

Dynamic thermal management techniques employ a set of on-chip thermal sensors to measure runtime thermal behavior of microprocessors so as to prevent the on-set of high temperatures. Therefore, effective analysis of thermal behavior and determination of the best allocation and placement of thermal sensors directly impact the effectiveness of the dynamic thermal management mechanisms. In this paper, we propose systematic and effective techniques for determining the fewest number of thermal sensors and the optimal locations based on dual clustering to provide a high fidelity thermal monitoring. Initially, we utilize the dual clustering algorithm to devise method that can reduce the number of sensors to a great extent while satisfying an expected accuracy. Then we identify an optimal physical location for each sensor such that the sensor’s attraction towards steep thermal gradient is maximized. Experimental results indicate the superiority of our techniques and confirm that our proposed methods are capable of creating a sensor distribution for a given microprocessor architecture using the number of thermal sensors of 2, 8, 15, 24, 35, depending on different expected hot spot temperature error accuracy of 5%, 4%, 3%, 2%, 1%, respectively.

Optimal Sensor Placement Methods for Active Power Electronic Systems

“…Optimal sensor placement has been studied extensively for thermal monitoring of low power density electronics (LPDEs) [26,27], such as microprocessors [28][29][30][31][32], multi-core processors, Network-on Chip (NoC) and System on Chip (SoC) devices [26,27,33,34], data centers [35], and cyber physical systems (CPSs) [36]. LPDEs are electronic devices where the power to volume ratio is small.…”

“…As a result, thermal consid-erations are not usually a significant design concern. Sensor placement strategies for power electronics have included uniform or non-uniform allocation [30], by grid-based or clustering techniques [28,33,37], distance minimization and greedy approaches [32,38] and entropy-based metrics [34].…”