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FPGA Task Arrangement with Genetic Algorithms
eBook
ISBN/EAN: 9783832422998
Umbreit-Nr.: 6009958
Sprache:
Englisch
Umfang: 144 S., 1.43 MB
Format in cm:
Einband:
Keine Angabe
Erschienen am 13.04.2000
Auflage: 1/2000
E-Book
Format: PDF
DRM: Nicht vorhanden
- Zusatztext
- Inhaltsangabe:Abstract:Two evolutionary approaches of allocating tasks onto a Field-Programmable Gate Array (FPGA) are presented.Offline task arrangement: whenever a set of tasks has to be arranged onto an FPGA in practice, one is interested in arranging a maximum number of tasks which efficiently utilize the FPGA area. A genetic algorithm is proposed searching for an arrangement of tasks offline, i.e. before the tasks are physically placed onto the FPGA.Online task arrangement: FPGAs that allow partial reconfiguration at run-time can be shared among multiple independent tasks. When the sequence of tasks to be performed is unpredictable the FPGA controller needs to make allocation decisions online. Since online allocation suffers from fragmentation, tasks can end up waiting despite there being sufficient, albeit non-contiguous resources available to service them. The time to complete tasks is consequently longer and the utilization of the FPGA is lower than it could be. A genetic algorithm is proposed rearranging a subset of the tasks executing on the FPGA when doing so allows the next pending task to be processed sooner. In comparison with other heuristic approaches a genetic algorithm is described and evaluated which overcomes the NP-hard problems of identifying feasible rearrangements and scheduling the rearrangements when moving tasks are reloaded from off-chip.Inhaltsverzeichnis:Table of Contents:1.Introduction72.Field Programmable Gate Arrays92.1Architecture of FPGAs92.2Dynamically Reconfigurable FPGAs102.3Comparison with Related Devices112.4Creation of an FPGA Model113.FPGA Task Arrangement Problem183.1Static Task Arrangement Problem183.1.1Static Task Management183.1.2Problem Formulation203.2Dynamic Task Arrangement Problem213.2.1Dynamic Task Management213.2.2Search for an Admissible Task Rearrangement223.2.3Rearrangement Scheduling243.2.4Buffer Restriction273.2.5Problem Formulation304.Arrangement Concepts314.1Shape Functions314.2Slicing Trees355.Genetic Algorithms415.1Introduction415.2The Functioning of Genetic Algorithms 435.3Main Components of Genetic Algorithms455.3.1Representation455.3.2Initialization465.3.3Evaluation475.3.4Stopping Condition475.3.5Reproduction475.3.6Selection485.3.7Genetic Operators496.Static Task Arrangement516.1Representation516.2Initialization526.2.1Random Pairing536.2.2Traversal of Flexible Slicing Trees536.3Evaluation566.4Genetic []
- Kurztext
- Inhaltsangabe:Abstract: Two evolutionary approaches of allocating tasks onto a Field-Programmable Gate Array (FPGA) are presented. Offline task arrangement: whenever a set of tasks has to be arranged onto an FPGA in practice, one is interested in arranging a maximum number of tasks which efficiently utilize the FPGA area. A genetic algorithm ...