An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models

Posted onAuthorEric VerbeekLeave a comment

Cuzzocrea, A., Mumolo, E., & Hassani, M. (2019). An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. In Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2953-2959). [8616498] Institute of Electrical and Electronics Engineers. DOI: 10.1109/SMC.2018.00502

Abstract

This paper proposes and experimentally assesses a machine learning approach for supporting the effective and efficient generation of synthetic memory reference traces for a wide range of application scenarios. The proposed approach makes a nice use of extended hierarchical Markov models.

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