Accurate Open-Set Recognition for Memory Workload

How can we accurately identify new memory workloads while classifying known workloads? Verifying DRAM (Dynamic Random Access Memory) using various is an important task to guarantee the quality of DRAM. A crucial component in process open-set recognition which aims detect not seen training phase. Despite its importance, however, existing methods are unsatisfactory terms accuracy since they fail exploit characteristics workload sequences. In this article, propose Acorn , accurate method capturing extracts two types feature vectors capture sequential patterns and spatial locality access. then uses classify a subsequence into one classes or it as unknown class. Experiments show that achieves state-of-the-art accuracy, giving up 37% points higher class detection achieving comparable classification than methods.