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
An Effective and Efficient Approach for Supporting the Generation of Synthetic Memory Reference Traces via Hierarchical Hidden/Non-Hidden Markov Models / Cuzzocrea, Alfredo Massimiliano; Mumolo, Enzo; Hassani, Marwan. - STAMPA. - (2018), pp. 2953-2959. ( 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Miyazaki, Japan 7–10 October 2018) [10.1109/SMC.2018.00502].
An Effective and Efficient Approach for Supporting the Generation of Synthetic Memory Reference Traces via Hierarchical Hidden/Non-Hidden Markov Models
Alfredo Cuzzocrea;Enzo Mumolo;
2018-01-01
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| File | Dimensione | Formato | |
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