Accurate Open-set Recognition for Memory Workload

TL;DR

This paper introduces Acorn, a novel open-set recognition method that effectively detects new memory workloads by capturing sequential and spatial features, significantly improving accuracy over existing techniques.

Contribution

Acorn is the first method to exploit workload sequence characteristics for open-set recognition, achieving state-of-the-art accuracy in identifying unknown memory workloads.

Findings

Up to 37% points higher unknown class detection accuracy.

Achieves comparable known class classification accuracy to existing methods.

Demonstrates effectiveness on DRAM workload verification tasks.

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.