Scheduling Periodic Jobs that Allow Imprecise Results
IEEE Transactions on Computers
Embedded program timing analysis based on path clustering and architecture classification
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Proceedings of the 6th international workshop on Hardware/software codesign
Optimal Reward-Based Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
System-Level Design Methods for Low-Energy Architectures Containing Variable Voltage Processors
PACS '00 Proceedings of the First International Workshop on Power-Aware Computer Systems-Revised Papers
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
IEEE Transactions on Parallel and Distributed Systems
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
Throughput Maximization for Intel Desktop Platform under the Maximum Temperature Constraint
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
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While performance-adaptable applications are gaining increased popularity on embedded systems (especially multimedia applications), efficient scheduling methods are necessary to explore such feature to achieve the most performance outcome. In addition to conventional scheduling requirements such as real-time and dynamic power, emerging challenges such as leakage power and multiprocessors further complicate the formulation and solution of adaptive application scheduling problems. In this paper, we propose a runtime adaptive application scheduling scheme that efficiently distributes the runtime slack in a task graph, to achieve maximized performance under timing and dynamic/leakage energy constraints. A guided-search heuristics is proposed to select the best-fit frequency levels that maximize the additional program cycles of adaptive tasks. Moreover, we devise a two-stage receiver task selection method that runs efficiently at runtime, in order to quickly find the slack distribution targets. Experiments on synthesized tasks and a JPEG2000 decoder are conducted to justify our approach. Results show that our method achieves at least 25% runtime performance increase compared to contemporary approaches, incurring negligible runtime overhead.