Dynamic scratch-pad memory management with data pipelining for embedded systems

Yanqin Yang, Meng Wang, Haijin Yan, Zili Shao, Minyi Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


In this paper, we propose an effective data pipelining technique, SPDP (Scratch-Pad Data Pipelining), for dynamic scratch-pad memory (SPM) management with DMA (Direct Memory Access). Our basic idea is to overlap the execution of CPU instructions and DMA operations. In SPDP, based on the iteration access patterns of arrays, we group multiple iterations into a block to improve the data locality of regular array accesses. We allocate the data of multiple iterations into different portions of the SPM. In this way, when the CPU executes instructions and accesses data from one portion of the SPM, DMA operations can be performed to transfer data between the of f-chip memory and another portion of SPM simultaneously. We perform code transformation to insert DMA instructions to achieve the data pipelining. We have implemented our SPDP technique with the IMPACT compiler, and conduct experiments using a set of loop kernels from DSPstone, Mibench, and Mediabench on the cycle-accurate VLIW simulator of Trimaran. The experimental results show that our technique achieves performance improvement compared with the previous work.
Original languageEnglish
Pages (from-to)1874-1892
Number of pages19
JournalConcurrency Computation Practice and Experience
Issue number13
Publication statusPublished - 10 Sept 2010


  • Data pipelining
  • Embedded systems
  • Scratch-pad memory management

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics


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