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How can we interpret predictions of a workload classification model? A workload is a sequence of operations executed in DRAM, where each operation contains a command and an address. Classifying a given sequence into a correct workload type is important for verifying the quality of DRAM. Although a previous model achieves a reasonable accuracy on workload classification, it is challenging to interpret the prediction results since it is a black box model. A promising direction is to exploit interpretation models which compute the amount of attribution each feature gives to the prediction. However, none of the existing interpretable models are tailored for workload classification. The main challenges to be addressed are to 1) provide interpretable features for further improving interpretability, 2) measure the similarity of features for constructing the interpretable super features, and 3) provide consistent interpretations over all instances. In this paper, we propose INFO (INterpretable model For wOrkload classification), a model-agnostic interpretable model which analyzes workload classification results. INFO provides interpretable results while producing accurate predictions. We design super features to enhance interpretability by hierarchically clustering original features used for the classifier. To generate the super features, we define and measure the interpretability-friendly similarity, a variant of Jaccard similarity between original features. Then, INFO globally explains the workload classification model by generalizing super features over all instances. Experiments show that INFO provides intuitive interpretations which are faithful to the original non-interpretable model. INFO also shows up to 2.0× faster running time than the competitor while having comparable accuracies for real-world workload datasets.我们应当如何解读工作负载分类模型的预测结果？工作负载指在动态随机存取存储器（DRAM，Dynamic Random Access Memory）中执行的操作序列，每条操作均包含一条命令与一个地址。将给定序列归类至正确的工作负载类型，对于验证动态随机存取存储器的运行质量至关重要。尽管已有模型在工作负载分类任务中取得了较为可观的分类精度，但由于其属于黑盒模型，解读其预测结果仍极具挑战。一个颇具前景的研究方向是借助可解释模型，计算每个特征对预测结果的归因贡献度。然而，目前尚无针对工作负载分类任务定制的可解释模型。亟待解决的核心挑战包括：1）提供可解释特征以进一步提升可解释性；2）量化特征间的相似度，以构建可解释的超级特征；3）为所有样本提供一致的可解释结果。本文提出INFO（面向工作负载分类的可解释模型，INterpretable model For wOrkload classification），这是一种模型无关的可解释模型，用于分析工作负载分类的结果。INFO在生成精准预测结果的同时，可输出具备可解释性的分析结果。我们通过对分类器所使用的原始特征进行层级聚类，设计了超级特征以增强可解释性。为生成超级特征，我们定义并量化了适配可解释性的相似度——一种基于原始特征间杰卡德相似度（Jaccard similarity）的改进形式。随后，INFO通过在全样本范围内泛化超级特征，实现对工作负载分类模型的全局解释。实验结果表明，INFO所输出的可解释结果直观易懂，且忠实于原始的非可解释模型。相较于同类竞品，INFO的运行速度最高可达其2.0倍，同时在真实世界工作负载数据集上的分类精度与之相当。