Abstract
A fully scalable, cellular multiprocessor architecture is proposed that is able to dynamically adapt its processing resources to varying demands of signal processing applications. This ability is achieved by migration of tasks between processor cells at run-time such as to avoid cell overload. Several dynamic migration strategies are investigated, and simulation results are provided for different load cases. The results indicate a potential performance gain from dynamic task migration on signal processing applications. By employing a rule-based learning system for an adaptive combination of migration strategies, the migration benefits become independent from the particular application characteristics.
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