A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis  

作　　者：Chen Wei-wei He Wei Zhu Hai-long Zhou Guo-hui Mu Quan-qi Han Peng 

机构地区：[1]College of Computer Science and Information Engineering,Harbin Normal University,Harbin,150500,China [2]Rocket Force University of Engineering,Xi’an,710025,China

出　　处：《Computers, Materials & Continua》2023年第3期6119-6143,共25页计算机、材料和连续体（英文）

基　　金：This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736;in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456;in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.

摘　　要：The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

关 键 词：Hierarchical belief rule base(HBRB) evidence reasoning(ER) INTERPRETABILITY global sensitivity analysis(GSA) whale optimization algorithm(WOA) 

分 类 号：TP332[自动化与计算机技术—计算机系统结构]