论文标题
准确的内存工作负载识别
Accurate Open-set Recognition for Memory Workload
论文作者
论文摘要
在对已知内存工作负载进行分类时，我们如何准确识别新的内存工作负载？使用各种工作负载验证DRAM（动态随机访问存储器）是确保DRAM质量的重要任务。该过程中的关键组成部分是开放式识别，旨在检测在训练阶段看不到的新工作负载。但是，尽管它很重要，但现有的开放式识别方法在准确性方面并不令人满意，因为它们无法利用工作负载序列的特征。在本文中，我们提出了Acorn，这是一种准确的开放式识别方法，可捕获工作负载序列的特征。橡子提取两种类型的特征向量，以捕获内存访问中的顺序模式和空间位置模式。然后，橡子使用特征向量将子序列准确地分类为已知类别之一，或将其识别为未知类别。实验表明，橡子达到了最先进的准确性，比现有方法比现有方法提高了未知类检测准确性高达37％的班级检测准确性。
How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not seen in the training phase. Despite its importance, however, existing open-set recognition methods are unsatisfactory in terms of accuracy since they fail to exploit the characteristics of workload sequences. In this paper, we propose Acorn, an accurate open-set recognition method capturing the characteristics of workload sequences. Acorn extracts two types of feature vectors to capture sequential patterns and spatial locality patterns in memory access. Acorn then uses the feature vectors to accurately classify a subsequence into one of the known classes or identify it as the unknown class. Experiments show that Acorn achieves state-of-the-art accuracy, giving up to 37% points higher unknown class detection accuracy while achieving comparable known class classification accuracy than existing methods.
