SDRM: simultaneous determination of regions and function-to-region mapping for scratchpad memories

  • Authors:

  • Amit Pabalkar;Aviral Shrivastava;Arun Kannan;Jongeun Lee

  • Affiliations:

  • Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ

  • Venue:
  • HiPC'08 Proceedings of the 15th international conference on High performance computing
  • Year:
  • 2008

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Abstract

Many programmable embedded systems feature low power processorscoupled with fast compiler controlled on-chip scratchpad memories (SPMs) toreduce their energy consumption. SPMs are more efficient than caches in termsof energy consumption, performance, area and timing predictability. However,unlike caches SPMs need explicit management by software, the quality ofwhich can impact the performance of SPM based systems. In this paper, wepresent a fully-automated, dynamic code overlaying technique for SPMs basedon pure static analysis. Static analysis is less restrictive than profiling and canbe easily extended to general compiler framework where the time consumingand expensive task of profiling may not be feasible. The SPM code mappingproblem is harder than bin packing problem, which is NP-complete. Therefore weformulate the SPMcode mapping as a binary integer linear programming problemand also propose a heuristic, determining simultaneously the region (bin) sizesas well as the function-to-region mapping. To the best of our knowledge, thisis the first heuristic which simultaneously solves the interdependent problemsof region size determination and the function-to-region mapping. We evaluateour approach for a set of MiBench applications on a horizontally split I-cache and SPM architecture (HSA). Compared to a cache-only architecture (COA),the HSA gives an average energy reduction of 35%, with minimal performancedegradation. For the HSA, we also compare the energy results from our proposedSDRM heuristic against a previous static analysis based mapping heuristic andobserve an average 27% energy reduction.