Name
Abstract | ||
|---|---|---|
This paper presents our work on extending artificial neural networks use for real-time task scheduling to heterogeneous System-on-Chip architectures. The Hopfield model is the neural network model considered in this study. We introduce new constructing rules to design neural network so that architecture heterogeneity can be considered. We show that these new rules ensure the network stabilization on states that take into account the architecture heterogeneity while meeting the imposed task constraints. |
Year | Venue | Keywords |
|---|---|---|
2007 | EUSIPCO | neural nets,scheduling,system-on-chip,task analysis,hopfield model,architecture heterogeneity,artificial neural networks,hardware task scheduling,heterogeneous soc architectures,network stabilization,convergence,scheduling algorithms,hardware,real time systems |
Field | DocType | ISBN |
Convergence (routing),Architecture,Fair-share scheduling,Scheduling (computing),Computer science,Stochastic neural network,Types of artificial neural networks,Time delay neural network,Artificial neural network,Distributed computing | Conference | 978-839-2134-04-6 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Imene Benkermi | 1 | 0 | 0.34 |
| Daniel Chillet | 2 | 193 | 26.12 |
| Pillement, Sebastien | 3 | 33 | 5.30 |
| Olivier Sentieys | 4 | 597 | 73.35 |