Adaptive Algorithm based on Renyi’s Entropy for Task Mapping in a Hierarchical Wireless Network-on-Chip Architecture
Abstract
This paper describes the use of Renyi’s
entropy as a way to improve the convergence time
of the Population-Based Incremental Learning (PBIL)
optimization algorithm. As a case study, the algorithm
was used in a hierarchical wireless network-on-chip
(WiNoC) for the sake of performing the optimal task
mapping of applications. Two versions of Renyi’s entropy
are used and compared to the more traditional Shannon
formulation. The obtained results are promising and
suggest that Renyi’s entropy may help to reduce the
PBIL convergence time, without degrading the quality of
the found solutions.
entropy as a way to improve the convergence time
of the Population-Based Incremental Learning (PBIL)
optimization algorithm. As a case study, the algorithm
was used in a hierarchical wireless network-on-chip
(WiNoC) for the sake of performing the optimal task
mapping of applications. Two versions of Renyi’s entropy
are used and compared to the more traditional Shannon
formulation. The obtained results are promising and
suggest that Renyi’s entropy may help to reduce the
PBIL convergence time, without degrading the quality of
the found solutions.
Keywords
Renyi’s entropy, PBIL, Wireless network-on-chip (WiNoC), Mapping, Convergence time.