مدیریت دمای پویا برای سیستم های چندهسته ای با کنترل فرکانس پردازنده و سرعت فن
Publish place: Tabriz Journal of Electrical Engineering، Vol: 49، Issue: 2
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
View: 363
This Paper With 13 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_TJEE-49-2_030
تاریخ نمایه سازی: 20 آذر 1398
Abstract:
Microprocessor designers use the design of multicore systems on a chip to increase their computing power. Adding the number of cores leads to an increase in the power density, followed by enhancement of temperature. Reactive and proactive approaches are two sets of the methods for managing the temperature. Unlike the reactive methods that act based on threshold temperature, proactive approaches utilize a thermal prediction model in thermal management. In this paper, two multilayer perceptron neural networks has been used for thermal prediction and temperature control. An appropriate dataset is provided for training each model. This dataset consists of some features that are read by sensors and measurement tools and new features that are produced by proposed processes. In this regard, historical features are suggested for thermal model. Proper features of thermal model are selected by using feature selection based on mutual information. The temperature is controlled by setting the processor frequency and fan speed. The features of control model are selected by non-dominated sorting genetic algorithm. The error of thermal model for different time distances is about 0.5 °C. The thermal control model has respectively 2% and 0.6% errors in determining the processor frequency and fan speed.
Keywords:
Authors
J. Mohebbi Najm Abad
Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran
A. Soleimani
Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :