| dc.description.abstract | In the aftermath of the 2018 US-China trade dispute and the subsequent impact of COVID-19, our country has experienced a significant increase in the manufacturing output of servers. The CPU, a crucial component within servers, plays a pivotal role in core operations, closely tied to the overall performance of server applications. With the introduction of a multitude of specifications for next-generation CPU products by manufacturers to meet the demands of various new applications, the verification burden on server development and experimental validation has increased. This necessitates rapid validation of various CPU product specifications while ensuring efficient resource utilization, particularly in the thermal testing process.
To address this challenge, this study employs various data mining methods to construct predictive models based on the analysis of the correlation between CPU thermal validation results and key data points. The objective is to identify the optimal predictive model applicable to server CPU thermal validation results. Early estimation of CPU thermal validation results allows the simplification of testing and validation procedures and related costs when making system design changes or configuring components with different specifications.
The research focuses on the thermal validation department of a leading US server manufacturing company, using actual thermal validation data and results from January 2020 to July 2023. The SAS Enterprise Miner (EM) software is employed to establish predictive models using five data mining methods: Logistic regression, Artificial neural network, Decision tree, Gradient Boosting Decision Tree, and Random forest. The overall predictive performance of these models is evaluated, and the Random Forest model is identified as relatively optimal, achieving a correct classification rate of 92.7% on the test set.
After establishing the predictive model, the SAS Enterprise Miner (EM) scoring prediction function is utilized to analyze and predict the classification results of the original data to be predicted. The results demonstrate the successful output of calculated probability values and predictions using the Random forest model. Through actual verification and comparison of ＂model predicted data＂ with ＂actual thermal validation data＂ classification results, the predictive capability of the Random forest model is confirmed to be relatively reliable. This indicates the practical applicability of the predictive model to forecast CPU thermal validation results for the case company.
Assuming the case company adopts the predictive model as part of the result determination method for some thermal validation experiments, the main effects include saving approximately 1400 hours of time consumption cost for verification over a one-year execution period for 20 systems. The secondary effects, such as enhancing the utilization of environmental testing chamber equipment and reducing labor costs associated with verification, result in additional benefits. In summary, the established predictive model, the Random forest, proves effective in assisting in improving the efficiency of server CPU thermal validation. However, it is acknowledged that there may be some limiting factors not fully addressed in the research and modeling process. Therefore, it is recommended that future researchers consider more potential influencing factors and include them as analysis items and content in their research. This includes continuously adjusting the model to reduce the misclassification rate by integrating predictive modeling with actual verification results, establishing predictive models for different cooling technologies, exploring and establishing GPU predictive models, analyzing the predictive capabilities of models established from databases of different server brands, and evaluating the use of other data mining techniques for analysis and modeling. | en_US |