Design of embedded compute-intensive processing elements and their scheduling in a reconfigurable environment

This paper addresses the problem of solving computationally intensive algorithms such as multimedia and graphics applications. A novel methodology to design embedded compute-intensive processing elements (ECIPEs) is proposed. In order to identify common data flow patterns among core data flow graphs (DFGs), a low-complexity and parallelism-aware common subgraph extraction algorithm is proposed. In addition, a reconfiguration-aware static scheduling technique to manage task and resource dependencies is proposed. To validate the success of this approach, estimates of reconfiguration times obtained by performing several experiments (on an assorted set of algorithms taken from media standards such as MPEG-4 and frequently used graphics algorithms) are provided, and the potential for reduction in the number of reconfiguration cycles is shown.