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Leakage-Aware Predictive Thermal Management for Multi-Core Systems Using Echo State Network
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( IF 2.9 ) Pub Date : 2020-07-01 , DOI: 10.1109/tcad.2019.2915316
Hai Wang , Xingxing Guo , Sheldon X.-D. Tan , Chi Zhang , He Tang , Yuan Yuan

Leakage power is becoming significant in new generation IC chips. As leakage power is nonlinearly related to temperature, it is challenging to manage the thermal behavior of today’s multicore systems, since thermal management becomes a nonlinear control problem. In this paper, a new predictive dynamic thermal management (DTM) method with neural network thermal model is proposed to naturally consider the inherent nonlinearity between leakage and temperature. We start with analyzing the problems of using recurrent neural network (RNN) to build the nonlinear thermal model, and point out that there is exploding gradient induced long-term dependencies problem, leading to large model prediction errors. Based on this analysis, we further propose to use echo state network (ESN), which is a special type of RNN, as the leakage-aware nonlinear thermal model. We theoretically and experimentally show that ESN achieves much higher accuracy by completely avoiding the long-term dependencies problem. On top of this nonlinear ESN thermal model, we propose a novel model predictive control (MPC) scheme called ESN MPC, which uses iterative steps to find the optimal future power recommendations for thermal management. Being able to consider the leakage-temperature nonlinear effects and equipped with advanced control technique, the new method achieves an overall high quality temperature management with smooth and accurate temperature tracking. The experimental results show the new method outperforms the state-of-the-art leakage-aware multicore DTM method in both temperature management quality and computing overhead.

中文翻译:

使用 Echo 状态网络的多核系统的泄漏感知预测热管理

在新一代 IC 芯片中,漏电变得越来越严重。由于泄漏功率与温度呈非线性关系,因此管理当今多核系统的热行为具有挑战性,因为热管理成为非线性控制问题。在本文中,提出了一种新的具有神经网络热模型的预测动态热管理 (DTM) 方法,以自然地考虑泄漏和温度之间的固有非线性。我们从分析使用循环神经网络(RNN)构建非线性热模型的问题入手,指出存在爆炸梯度引起的长期依赖问题,导致模型预测误差较大。基于此分析,我们进一步建议使用回波状态网络 (ESN),这是一种特殊类型的 RNN,作为泄漏感知非线性热模型。我们从理论上和实验上表明,通过完全避免长期依赖问题,ESN 实现了更高的准确度。在这种非线性 ESN 热模型之上,我们提出了一种称为 ESN MPC 的新型模型预测控制 (MPC) 方案,该方案使用迭代步骤来寻找热管理的最佳未来功率建议。新方法能够考虑泄漏温度非线性效应,并配备先进的控制技术,实现了全面高质量的温度管理,温度跟踪平稳准确。实验结果表明,新方法在温度管理质量和计算开销方面均优于最先进的泄漏感知多核 DTM 方法。
更新日期:2020-07-01
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