Hot Spot Identification and System Parameterized Thermal Modeling for Multi-Core Processors Through Infrared Thermal Imaging
Hot Spot Identification and System Parameterized Thermal Modeling for Multi-Core Processors Through Infrared Thermal Imaging
复制标题
通过红外热成像进行多核处理器的热点识别和系统参数化热建模
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Tan
中科院分区：
文献类型：
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作者：
Sheriff Sadiqbatcha;Hengyang Zhao;H. Amrouch;J. Henkel;S. Tan
适合于系统级动态热、功率和可靠性调节和管理的精确热模型对于许多商用多核处理器至关重要。然而，由于缺乏信息和可用工具，为商用处理器开发如此精确的热模型并确定相关的热功率相关空间位置是一项具有挑战性的任务。现有的工具，如hotspot类热模型，对于在线应用程序可能存在不准确或效率低下的问题，主要是因为大多数工具依赖于无法精确量化的参数，例如功率走线，而其他工具则是不适合运行时使用的数值方法。在这项工作中，我们提出了一种利用红外热成像装置自动检测商用多核微处理器上主要热源的新方法。我们的方法涉及许多步骤，包括用于在测量的热图上降低噪声的二维离散余弦变换滤波器，以及用于热源识别的拉普拉斯变换之后的k -均值聚类。由于确定的热源是模具的热脆弱区域，我们提出了一种新的方法来推导能够预测其运行期间温度的热模型。我们使用长短期记忆（LSTM）网络建立一个动态热模型，该模型使用系统级变量，如芯片频率，电压和指令计数作为输入。该模型是专门使用商业多核处理器测量的热数据进行训练和测试的。实验结果表明，所提出的热模型在预测芯片上所有识别热源的温度方面具有很高的精度（均方根误差为2.04℃~ 2.57℃）。
Accurate thermal models suitable for system level dynamic thermal, power and reliability regulation and management are vital for many commercial multi-core processors. However, developing such accurate thermal models and identifying the related thermal-power relevant spatial locations for commercial processors is a challenging task due to the lack of information and available tools. Existing tools such as HotSpot-like thermal models may suffer from inaccuracy or inefficiency for online applications, primarily because most rely on parameters that cannot be precisely quantified, such as power-traces, while others are numerical methods not suitable for runtime use. In this work, we propose a novel approach to automatically detecting the major heat-sources on a commercial multi-core microprocessor using an infrared thermal imaging setup. Our approach involves a number of steps including 2D discrete cosine transformation filter for noise reduction on the measured thermal maps, and Laplacian transformation followed by K-mean clustering for heat-source identification. Since the identified heat-sources are the thermally vulnerable areas of the die, we propose a novel approach to deriving a thermal model capable of predicting their temperatures during runtime. We apply Long-Short-Term-Memory (LSTM) networks to build a dynamic thermal model which uses system-level variables such as chip frequency, voltage and instruction count as inputs. The model is trained and tested exclusively using measured thermal data from a commercial multi-core processor. Experimental results show that the proposed thermal model achieves very high accuracy (root-mean-square-error: 2.04°C to 2.57° C) in predicting the temperature of all the identified heat-sources on the chip.