research-article

Accurate Open-Set Recognition for Memory Workload

Authors:

U KangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 9

Article No.: 126, Pages 1 - 14

https://doi.org/10.1145/3597027

Published: 15 June 2023 Publication History

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 article, 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.

References

[1]

2013. SDRAM STANDARD. (2013). Retrieved from https://www.jedec.org/sites/default/files/docs/JESD79-4.pdf.

[2]

Muhammad Ali, Stavros Shiaeles, Gueltoum Bendiab, and Bogdan Ghita. 2020. MALGRA: Machine learning and n-gram malware feature extraction and detection system. Electronics 9, 11 (2020), 1777.

[3]

Abhijit Bendale and Terrance E. Boult. 2016. Towards open set deep networks. In CVPR. IEEE Computer Society, 1563–1572.

[4]

Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. 2020. A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems 31, 5 (2020), 1747–1756.

[5]

Mehadi Hassen and Philip K. Chan. 2020. Learning a neural-network-based representation for open set recognition. In SDM. SIAM, 154–162.

[6]

Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.

[7]

Jun-Gi Jang, Dongjin Choi, Jinhong Jung, and U. Kang. 2018. Zoom-SVD: Fast and memory efficient method for extracting key patterns in an arbitrary time range. In CIKM. ACM, 1083–1092.

[8]

Jun-Gi Jang and U. Kang. 2020. D-tucker: Fast and memory-efficient tucker decomposition for dense tensors. In ICDE. IEEE, 1850–1853.

[9]

Ian Jolliffe. 2002. Principal Component Analysis. Wiley Online Library.

[10]

Dongha Lee, Sehun Yu, and Hwanjo Yu. 2020. Multi-class data description for out-of-distribution detection. In KDD. ACM, 1362–1370.

[11]

Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In NeurIPS 2018. 7167–7177.

[12]

Zhihui Li, Lina Yao, Xiaojun Chang, Kun Zhan, Jiande Sun, and Huaxiang Zhang. 2019. Zero-shot event detection via event-adaptive concept relevance mining. Pattern Recognition 88 (2019), 595–603.

Digital Library

[13]

Shiyu Liang, Yixuan Li, and R. Srikant. 2018. Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR. OpenReview.net.

[14]

Weitang Liu, Xiaoyun Wang, John D. Owens, and Yixuan Li. 2020. Energy-based Out-of-distribution detection. In NeurIPS.

[15]

Minnan Luo, Xiaojun Chang, Liqiang Nie, Yi Yang, Alexander G. Hauptmann, and Qinghua Zheng. 2018. An adaptive semisupervised feature analysis for video semantic recognition. IEEE Transactions on Cybernetics 48, 2 (2018), 648–660.

[16]

James Mutinda, Waweru Mwangi, and George Okeyo. 2021. Lexicon-pointed hybrid n-gram features extraction model (LeNFEM) for sentence level sentiment analysis. Engineering Reports 3, 8 (2021), e12374.

[17]

Stanisław Osiński, Jerzy Stefanowski, and Dawid Weiss. 2004. Lingo: Search results clustering algorithm based on singular value decomposition. In Intelligent Information Processing and Web Mining. Springer, 359–368.

[18]

Poojan Oza and Vishal M. Patel. 2019. C2AE: Class conditioned auto-encoder for open-set recognition. In CVPR. Computer Vision Foundation, IEEE, 2307–2316.

[19]

Walter J. Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E. Boult. 2013. Toward open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 7 (2013), 1757–1772.

Digital Library

[20]

Sooyeon Shim, Doyeon Kim, Jun-Gi Jang, Suhyun Chae, Jeeyong Lee, and U. Kang. 2023. Fast and accurate interpretation of workload classification model. Plos One 18, 3 (2023), e0282595.

[21]

Lei Shu, Hu Xu, and Bing Liu. 2017. DOC: Deep open classification of text documents. In EMNLP. Association for Computational Linguistics, 2911–2916.

[22]

Krzysztof Simek, Krzysztof Fujarewicz, Andrzej Świerniak, Marek Kimmel, Barbara Jarzab, Małgorzata Wiench, and Joanna Rzeszowska. 2004. Using SVD and SVM methods for selection, classification, clustering and modeling of DNA microarray data. Engineering Applications of Artificial Intelligence 17, 4 (2004), 417–427.

[23]

Xin Sun, Zhenning Yang, Chi Zhang, Keck Voon Ling, and Guohao Peng. 2020. Conditional gaussian distribution learning for open set recognition. In CVPR. IEEE, 13477–13486.

[24]

Yiyou Sun, Chuan Guo, and Yixuan Li. 2021. React: Out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems 34 (2021), 144–157.

[25]

Andrija Tomovic, Predrag Janicic, and Vlado Keselj. 2006. N-gram-based classification and unsupervised hierarchical clustering of genome sequences. Comput. Methods Programs Biomed. 81, 2 (2006), 137–153.

[26]

Michael E. Wall, Andreas Rechtsteiner, and Luis M. Rocha. 2003. Singular value decomposition and principal component analysis. In A Practical Approach to Microarray Data Analysis. Springer, 91–109.

[27]

Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. 2019. Knowledge extraction with no observable data. In NeurIPS. 2701–2710.

[28]

Jaemin Yoo, Hyunsik Jeon, and U. Kang. 2019. Belief propagation network for hard inductive semi-supervised learning. In IJCAI, Sarit Kraus (Ed.). ijcai.org, 4178–4184.

[29]

Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, and Takeshi Naemura. 2019. Classification-reconstruction learning for open-set recognition. In CVPR. Computer Vision Foundation, IEEE, 4016–4025.

[30]

Hanqi Zhang, Xi Xiao, Francesco Mercaldo, Shiguang Ni, Fabio Martinelli, and Arun Kumar Sangaiah. 2019. Classification of ransomware families with machine learning based on N-gram of opcodes. Future Generation Computer Systems 90 (2019), 211–221.

[31]

Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan, and Viktor K. Prasanna. 2022. Fine-grained address segmentation for attention-based variable-degree prefetching. In CF’22: 19th ACM International Conference on Computing Frontiers, Turin, Italy, May 17-22, 2022. ACM, 103–112.

Digital Library

Cited By

Shim SKim DJang JChae SLee JKang U(2023)Fast and accurate interpretation of workload classification modelPLOS ONE10.1371/journal.pone.028259518:3(e0282595)Online publication date: 6-Mar-2023
https://doi.org/10.1371/journal.pone.0282595
Thanh LAbed-Meraim KTrung NHafiane A(2023)Tracking online low-rank approximations of higher-order incomplete streaming tensorsPatterns10.1016/j.patter.2023.1007594:6(100759)Online publication date: Jun-2023
https://doi.org/10.1016/j.patter.2023.100759

Index Terms

Accurate Open-Set Recognition for Memory Workload
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Hardware
  1. Integrated circuits
    1. Semiconductor memory
      1. Dynamic memory

Recommendations

Variance-Aware Bi-Attention Expression Transformer for Open-Set Facial Expression Recognition in the Wild
MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Despite the great accomplishments of facial expression recognition (FER) models in closed-set scenarios, they still lack open-world robustness when it comes to handling unknown samples. To address the demands of operating in an open environment, open-set ...
Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face ...
Power management of hybrid DRAM/PRAM-based main memory
DAC '11: Proceedings of the 48th Design Automation Conference

Hybrid main memory consisting of DRAM and non-volatile memory is attractive since the non-volatile memory can give the advantage of low standby power while DRAM provides high performance and better active power. In this work, we address the power ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 9

November 2023

373 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3604532

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2023

Online AM: 15 May 2023

Accepted: 06 May 2023

Revised: 05 November 2022

Received: 02 April 2022

Published in TKDD Volume 17, Issue 9

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Samsung Electronics Co., Ltd.
The Institute of Engineering Research and ICT at Seoul National University

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
204
Total Downloads

Downloads (Last 12 months)130
Downloads (Last 6 weeks)8

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shim SKim DJang JChae SLee JKang U(2023)Fast and accurate interpretation of workload classification modelPLOS ONE10.1371/journal.pone.028259518:3(e0282595)Online publication date: 6-Mar-2023
https://doi.org/10.1371/journal.pone.0282595
Thanh LAbed-Meraim KTrung NHafiane A(2023)Tracking online low-rank approximations of higher-order incomplete streaming tensorsPatterns10.1016/j.patter.2023.1007594:6(100759)Online publication date: Jun-2023
https://doi.org/10.1016/j.patter.2023.100759

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents