| dc.description.abstract | With technology’s revolution and innovation, CPU frequency and computing capability become more and more higher and faster. But, at same time, we also have to face the problem of high temperature and heat dissipation that generated by CPU. Generally, there are two common solutions for CPU heat dissipation (based on the types of heatsinks): (1) Active Heatsinks: Consists of one PWM fan and one heatsinks, and control the airflow of heatsinks via variable PWM fan speed; (2) Passive Heatsinks: Larger than Active Heatsinks, and require in-depth knowledge of the airflow and air-ducting techniques to manage the airflow in the chassis. Consider cost and convenience, usually, Active Heatsinks solution will be adopted on the most PC or own-brand computer, and Passive Heatsink is mainly used on the system (ex. Server) with multi-heat sources.
Since Active Heatsinks consist of two parts - PWM fan and heatsinks, motherboard vendors usually have to spend lots of man-power、time and special equipments in their experiments for finding out the best thermal solution (like material and structure of heatsinks, corresponding fan speed…etc) during product design phrase and make sure the performance of that thermal solution can meet CPU thermal specifications. However, in order to support all CPU(s) that in CPU support list and shorten the lead-time, the mother board vendors often adopt a strategy - design the thermal solution of their products to fulfill the CPU with maximum power in the CPU support list. This strategy guarantees their products are able to meet every CPU’s thermal specifications always but it could cause Over-Cooling problem. Unnecessary power consumption and higher noises could be caused.
In this thesis, the methods of CPU Fan Speed Optimization and Heat Sink Status Detection will be presented. Two RBFN (Radial Basis Function Networks) will be used. The first RBFN (Radial Basis Function Networks) will be used for modeling a CPU thermal model with the CPU thermal data we collect. And, a set of data for training second RBFN will be generated by using the combination of the first RBFN and CPU Thermal Profile. After second RBFN training finish, it will be integrated into a CPU fan speed control application program and used to control CPU fan speed continually and smartly under OS (operating system). And, the first RBFN will be used for detecting the CPU heatsinks status. End-user will not need to open the chassis for checking the CPU heatsinks status anymore.
To demonstrate the performance of the proposed methods, two CPU(s) with different TDP have been tested on two different systems with RBFN fan speed controller and original thermal solutions separately. And, the test result shows the RBFN fan speed controller can reduce 18.65% (ASUS+E4600)、56.03% (ASUS+X3220)、45.81% (GIGABYTE+E4600)、1.81% (GIGABYTE+X3220) fan speed (on average) than original thermal solutions(fan power consumption and fan noise can be lowered too) . And, the average predictability of RBFN Heat Sink Status Detector are 98.20% (ASUS+E4600)、94.71% (ASUS+X3220)、98.24% (GIGABYTE+E4600)、98.40% (GIGABYTE+X3220) without fan mask.
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