基于近似置信规则库的处理器性能预测研究

Research on Processor Performance Prediction Based on Approximate Confidence Rule Library

准确预测处理器性能对计算机硬件设计与改进有着重要意义。然而,处理器预测系统存在两个核心问题:预测过程中处理器内部构造复杂和不确定性以及预测结果的不可解释性。置信规则库作为一种基于IF-THEN规则的建模方法,具有一定的可解释性并且可以处理复杂系统评估与预测中的不确定信息。但BRB的规则爆炸问题限制了专家知识的使用。因此,本文提出了一种基于近似置信规则库(ABRB)的处理器性能预测模型。该模型通过构建单属性BRB模型来解决规则爆炸问题,并通过基于投影协方差矩阵自适应进化策略(P-CMA-ES)算法对专家知识给定的初始参数进行优化。最后以UCI中处理器数据集为例,验证了所提方法的有效性。Accurate prediction of processor performance is important for computer hardware design and improvement. However, there are two core problems in processor prediction systems: the complexity and uncertainty of processor internals during the prediction process and the non-interpretability of the prediction results. Belief rule base (BRB), as a modelling method based on IF-THEN rules, has some interpretability and can handle uncertain information in the evaluation and prediction of complex systems. However, the rule explosion problem of BRB limits the use of expert knowledge. Therefore, this paper proposes a processor performance prediction model based on approximate belief rule base. The model solves the rule explosion problem by constructing a single-attribute BRB model and optimizes the initial parameters given by the expert knowledge by the Projection Covariance Matrix Adaptive Evolutionary Strategy (P-CMA-ES) based algorithm. Finally, the effectiveness of the proposed method is validated using the UCI mid-processor dataset as an example.