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Evaluating a DVS Scheme for Real-Time Embedded Systems. Ruibin Xu, Daniel Mossé and Rami Melhem. Introduction. Energy conservation is important for real-time embedded systems Dynamic Voltage Scaling (DVS) is effective in power management
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Evaluating a DVS Scheme for Real-Time Embedded Systems Ruibin Xu, Daniel Mossé and Rami Melhem
Introduction • Energy conservation is important for real-time embedded systems • Dynamic Voltage Scaling (DVS) is effective in power management • A popular problem: minimizing energy consumption while meeting the deadlines
Frame length time Focus • Frame-based systems that execute variable workloads • The problem becomes minimizing the expected energy consumption while meeting the deadlines ……
A New DVS Scheme (MEEC) emsoft’05 original problem relax simplified problem efficient algorithm Evaluations fix practical solution optimal solution parc’05
Task and System Model • N periodic tasksT1, T2, …, TN to be executed consecutively in each frame • The power function is p(f) = c0+c1f α
slack slack slack Review of Existing Schemes Proportional Scheme Greedy Scheme Statistical Scheme
The MEEC Scheme • Incorporates the variability of the tasks into the speed schedule • The variability of the tasks are captured by the probability density function of the workload of the tasks • Aims to minimize the expected energy consumption in the system probability workload
β2 β3 β4 β1d (1-β1)d d The MEEC Scheme slack β1
… d d are Both are proportional to 1/d2 An Important Property The optimal expected energy consumption for
β4=100% β3=xx% vs. β2=xx% vs. β1=xx% vs. Computing βi T1 T2 T3 T4
Applying PACE • PACE is a technique in which the execution speed is gradually increased as the task progresses
The MEEC Scheme • The β values (optimal) are computed based on the assumption of unrestricted continuous frequency • We need to deal with: • Minimum and maximum speed restriction • Discrete speed • We have solutions and will use simulation to test them
Evaluations – Power models • Synthetic processor • Strictly conforms to p(f)=f3 • 10 frequencies: 100MHz, 200MHz,…, 1000MHz • Intel Xscale • Power numbers from Intel datasheets • p(f) = 80+1520(f/1000)3
Evaluation – Synthetic Workload • We simulated systems that have 5,10,15,20 tasks • The WCEC of each task is randomly generated from 10M to 1G cycles • The probability distribution of each task is randomly chosen from 6 representative distributions • Frame length
Evaluation – Synthetic Workload • We evaluated 8 schemes • Proportional with and without PACE • Greedy with and without PACE • Statistical with and without PACE • MEEC with and without PACE • We simulated 100,000 frames and computed the average energy consumption per frame for each scheme
Results – Synthetic Workload • For synthetic CPU, the best scheme is always MEEC (with or without PACE), but MEEC with PACE is only better than MEEE without PACE 13.6% of the time with an average saving of 1.2% • For Intel Xscale, the best scheme is always MEEC without PACE • Conclusion: PACE is not recommended in the MEEC scheme
β values Can differ a lot compute PACE (discrete frequency) fix Why PACE Is Not Good in MEEC scheme? PACE (under the assumption of unrestricted continuous frequency)
Evaluation – Automatic Target Recognition (ATR) • The ATR application does pattern matching of targets in images • The regions of interest (ROI) in the image are detected and each ROI is compared with all the templates • Image processing time is proportional to the number of ROIs
Evaluation – Automatic Target Recognition (ATR) • A front-end is responsible for collecting images and send them to the back-end periodically for target recognition • This application can be modeled as a frame-based real-time system in which all the tasks have the same workload distribution front-end …… back-end
Evaluation – Automatic Target Recognition (ATR) • Simulation setup • Use Intel Xscale • The period is 100ms • The front-end sends 1 to 6 images to the back-end • The number of ROIs in an image varies from 1 to 8 • The back-end precomputes 6 speed schedules
Summary • In this paper, we demonstrate and evaluate a new DVS scheme that aims to minimize the expected energy consumption in the system
Conclusions • The MEEC scheme achieves significant energy savings over the existing schemes • Using only static information or aggregating dynamic information, even with probabilistic techniques, will not produce as good results as when dynamic information for each task in considered separately
A Simple Example • 3 tasks, the frame length is 14 time units • For the CPU, c0=0, c1=1, fmin=0, and fmax=1