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.
翻译:在对已知的记忆工作量进行分类时,我们如何准确确定新的记忆工作量?利用各种工作量核查DRAM(动态随机存取记忆)是一项重要任务,以保证DRAM的质量。这一过程的一个关键组成部分是公开确认,目的是发现培训阶段没有看到的新工作量。尽管其重要性很重要,但现有的开放确认方法在准确性方面并不令人满意,因为它们未能利用工作量序列的特性。在本文中,我们建议采用一个准确的开放识别方法,即Arcorn,一种准确的开放识别方法,能够反映工作量序列的特点。在存取过程中,Aornoper提取了两类特性矢量,以捕捉到连续模式和空间位置模式。Arcorn随后利用特性矢量将子序列精确分类为已知类别之一,或将其确定为未知类别。实验显示,Arcorn取得了最新精度的精确度,给了37%的未知等级检测精确度,同时实现了可比的已知分类精确度。