Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Predicting MPEG execution times
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Dynamic power management using adaptive learning tree
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
An adaptive algorithm for low-power streaming multimedia processing
Proceedings of the conference on Design, automation and test in Europe
Dynamic voltage scheduling technique for low-power multimedia applications using buffers
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
A profile-based energy-efficient intra-task voltage scheduling algorithm for real-time applications
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management: Design Techniques and CAD Tools
Contents provider-assisted dynamic voltage scaling for low energy multimedia applications
Proceedings of the 2002 international symposium on Low power electronics and design
Control-theoretic dynamic frequency and voltage scaling for multimedia workloads
CASES '02 Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems
Intra-Task Voltage Scheduling for Low-Energy, Hard Real-Time Applications
IEEE Design & Test
Dynamic Power Management for Nonstationary Service Requests
IEEE Transactions on Computers
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Energy reduction techniques for multimedia applications with tolerance to deadline misses
Proceedings of the 40th annual Design Automation Conference
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Dynamic Voltage Scheduling Using Adaptive Filtering of Workload Traces
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Dynamic Voltage Scaling on MPEG Decoding
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
Variable voltage task scheduling algorithms for minimizing energy/power
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Reducing Multimedia Decode Power using Feedback Control
ICCD '03 Proceedings of the 21st International Conference on Computer Design
Profile-based optimal intra-task voltage scheduling for hard real-time applications
Proceedings of the 41st annual Design Automation Conference
Off-chip latency-driven dynamic voltage and frequency scaling for an MPEG decoding
Proceedings of the 41st annual Design Automation Conference
Hierarchical Adaptive Dynamic Power Management
IEEE Transactions on Computers
Workload prediction and dynamic voltage scaling for MPEG decoding
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Optimizing intra-task voltage scheduling using data flow analysis
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Dynamic voltage scaling for multitasking real-time systems with uncertain execution time
GLSVLSI '06 Proceedings of the 16th ACM Great Lakes symposium on VLSI
Program Counter-Based Prediction Techniques for Dynamic Power Management
IEEE Transactions on Computers
Control theory-based DVS for interactive 3D games
Proceedings of the 45th annual Design Automation Conference
Dynamic voltage scaling of supply and body bias exploiting software runtime distribution
Proceedings of the conference on Design, automation and test in Europe
Extended MPEG Video Format for Efficient Dynamic Voltage Scaling
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Dynamic voltage scaling of mixed task sets in priority-driven systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Energy-aware task scheduling with task synchronization for embedded real-time systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Improving energy efficiency for mobile platforms by exploiting low-power sleep states
Proceedings of the 9th conference on Computing Frontiers
Self-adaptive on-chip system based on cross-layer adaptation approach
International Journal of Reconfigurable Computing
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Dynamic voltage scaling (DVS) is a popular energy-saving technique for real-time tasks. The effectiveness of DVS critically depends on the accuracy of workload estimation, since DVS exploits the slack or the difference between the deadline and execution time. Many existing DVS techniques are profile based and simply utilize the worst-case or average execution time without estimation. Several recent approaches recognize the importance of workload estimation and adopt statistical estimation techniques. However, these approaches still require extensive profiling to extract reliable workload statistics and furthermore cannot effectively handle time-varying workloads. Feedback-control-based adaptive algorithms have been proposed to handle such nonstationary workloads, but their results are often too sensitive to parameter selection. To overcome these limitations of existing approaches, we propose a novel workload estimation technique for DVS. This technique is based on the Kalman filter and can estimate the processing time of workloads in a robust and accurate manner by adaptively calibrating estimation error by feedback. We tested the proposed method with workloads of various characteristics extracted from eight MPEG video clips. To thoroughly evaluate the performance of our approach, we used both a cycle-accurate simulator and an XScale-based test board. Our simulation result demonstrates that the proposed technique outperforms the compared alternatives with respect to the ability to meet given timing and Quality of Service constraints. Furthermore, we found that the accuracy of our approach is almost comparable to the oracle accuracy achievable only by offline analysis. Experimental results indicate that using our approach can reduce energy consumption by 57.5% on average, only with negligible deadline miss ratio (DMR) around 6.1%. Moreover, the average of computational overheads for the proposed technique is just 0.3%, which is the minimum value compared to other methods. More importantly, the DMR of our method is bounded by 11.7% in the worst case, while those of other methods are twice or more than ours.