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

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
2018
https://ieeexplore.ieee.org/document/8616498
File in questo prodotto:
File Dimensione Formato  
08616498.pdf

Accesso chiuso

Descrizione: Articolo presentato in Conferenza
Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 630.96 kB
Formato Adobe PDF
630.96 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2936913
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact