Task transition scheduling for data-adaptable systems

Nathan Sandoval, Casey Mackin, Sean Whitsitt, Vijay Shankar Gopinath, Sachidanand Mahadevan, Andrew Milakovich, Kyle Merry, Jonathan Sprinkle, Roman Lysecky

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Data-adaptable embedded systems operate on a variety of data streams, which requires a large degree of configurability and adaptability to support runtime changes in data stream inputs. Data-adaptable reconfigurable embedded systems, when decomposed into a series of tasks, enable a flexible runtime implementation in which a system can transition the execution of certain tasks between hardware and software while simultaneously continuing to process data during the transition. Efficient runtime scheduling of task transitions is needed to optimize system throughput and latency of the reconfiguration and transition periods. In this article, we provide an overview of a runtime framework enabling the efficient transition of tasks between software and hardware in response to changes in system inputs. We further present and analyze several runtime transition scheduling algorithms and highlight the latency and throughput tradeoffs for two dataadaptable systems. To evaluate the task transition selection algorithms, a case study was performed on an adaptable JPEG2000 implementation as well as three other synchronous dataflow systems characterized by transition latency and communication load.

Original languageEnglish (US)
Article number105
JournalACM Transactions on Embedded Computing Systems
Issue number4
StatePublished - May 2017


  • Data adaptability
  • Hardware/software codesign
  • Model-based design
  • Runtime transition scheduling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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