Design-Space Exploration for Block-Processing Based TemporalPartitioning of Run-Time Reconfigurable Systems

  • Authors:
  • Meenakshi Kaul;Ranga Vemuri

  • Affiliations:
  • Laboratory for Digital Design Environments, Department of ECECS, P.O. Box 210030, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Laboratory for Digital Design Environments, Department of ECECS, P.O. Box 210030, University of Cincinnati, Cincinnati, OH 45221-0030, USA

  • Venue:
  • Journal of VLSI Signal Processing Systems - Special issue on VLSI on custom computing technology
  • Year:
  • 2000

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Abstract

The reconfiguration capability of modern FPGA devices can beutilized to execute an application by partitioning it into multiplesegments such that each segment is executed one after the other onthe device. This division of an application into multiplereconfigurable segments is called temporal partitioning. Wepresent an automated temporal partitioning technique for acyclicbehavior level task graphs. To be effective, any behavior-levelpartitioning method should ensure that each temporal partition meetsthe underlying resource constraints. For this, a knowledge of theimplementation cost of each task on the hardware should be known.Since multiple implementations of a task that differ in area anddelay are possible, we perform design-space exploration tochoose the best implementation of a task from among the availableimplementations.To overcome the high reconfiguration overheadof the current day FPGA devices, we propose integration of thetemporal partitioning and design space exploration methodology withblock-processing. Block-processing is used to processmultiple blocks of data on each temporal partition so as to amortizethe reconfiguration time. We focus on applications that can berepresented as task graphs that have to be executed many times over alarge set of input data. We have integrated block-processing in thetemporal partitioning framework so that it also influences the designpoint selection for each task. However, this does not exclude usageof our system for designs for which block-processing is not possible.For both block-processing and non block-processing designs ouralgorithm selects the best possible design point to minimize theexecution time of the design.We present an ILP-basedmethodology for the integrated temporal partitioning, design spaceexploration and block-processing technique that is solved tooptimality for small sized design problems and in an iterativeconstraint satisfaction approach for large sized design problems. Wedemonstrate with extensive experimental results for the DiscreteCosine Transform (DCT) and random graphs the validity of ourapproach.