  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.
