Accurate Open-set Recognition for Memory Workload

• URL: http://arxiv.org/abs/2212.08817v1
• Date: Sat, 17 Dec 2022 07:37:40 GMT
• Title: Accurate Open-set Recognition for Memory Workload
• Authors: Jun-Gi Jang, Sooyeon Shim, Vladimir Egay, Jeeyong Lee, Jongmin Park, Suhyun Chae, U Kang
• Abstract summary: We propose Acorn, an accurate open-set recognition method capturing the characteristics of workload sequences. Experiments show that Acorn achieves state-of-the-art accuracy, giving up to 37% points higher unknown class detection accuracy.
• Score: 17.700081071282398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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