Configuration Mapping Algorithms to Reduce Energy and Time Reconfiguration Overheads in Reconfigurable Systems
In spite of the increasing success of reconfigurable hardware, the dynamic reconfiguration can introduce important overheads, both in terms of energy consumption and time, especially when configurations are fetched from an external memory. In order to address this problem, this article presents a co...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | español |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/34776 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/34776 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.312 FPGA Configuration Energy Consumption Configuration Time Overheads Configuration mapping. Informática (Informática) Hardware 1203.17 Informática |
| Sumario: | In spite of the increasing success of reconfigurable hardware, the dynamic reconfiguration can introduce important overheads, both in terms of energy consumption and time, especially when configurations are fetched from an external memory. In order to address this problem, this article presents a configuration memory hierarchy including two on-chip memory modules with different access time and energy consumption features. In addition, we have developed two configuration mapping algorithms that take advantage of these memories to reduce the system energy consumption, while increasing its performance. The first algorithm has been optimized for systems with reduced dynamic behavior, hence it optimizes the system for each given set of tasks. The second algorithm targets dynamic systems where the active tasks change unpredictably. Thus, its objective is also to decrease the pressure on the on-chip memories to reduce capacity conflicts. The presented results will demonstrate that, with the proper management, our configuration memory hierarchy leads to an energy consumption reduction up to 81% with respect to fetching the configurations from the external memory, while keeping the system performance very close to the ideal upperbound one. |
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