Runtime temperature-based power estimation for optimizing throughput of thermal-constrained multi-core processors

Technology scaling has allowed integration of multiple cores into a single die. However, high power consumption of each core leads to very high heat density, limiting the throughput of thermal-constrained multi-core processors. To maximize the throughput, various software-based dynamic thermal management and optimization techniques have been proposed, many of which depend on accurate temperature sensing of each core. However, the decision for dynamic thermal management and throughput optimization only based on the temperature of each core can result in less optimal throughput in certain circumstances according to our investigation. In this paper, we propose 1) a dynamic power estimation method using a single thermal sensor for each core in multi-core processors, 2) a die temperature reconstruction method using the estimated power, and 3) a throughput optimization method based the estimated power instead of the temperature. According to our experiment using 90nm technology, the proposed method results in less than 3% error in estimating power and hot-spot temperature of a multicore processor. Furthermore, the proposed throughput optimization method based on the estimated power leads to up to 4% higher throughput than a temperature-based optimization method.