Evaluating a DVS Scheme for Real-Time Embedded Systems

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Evaluating a DVS Scheme for Real-Time Embedded
Systems

• Ruibin Xu, Daniel Mossé and Rami Melhem

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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

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Focus

• Frame-based systems that execute variable
  workloads
• The problem becomes minimizing the expected
  energy consumption while meeting the deadlines

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A New DVS Scheme (MEEC)
emsoft05
simplified problem
original problem
relax
efficient algorithm
Evaluations
fix
optimal solution
practical solution
parc05
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Task and System Model

• N periodic tasksT1, T2, , TN to be executed
  consecutively in each frame
• The power function is p(f) c0c1f a

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Review of Existing Schemes
Proportional Scheme
Greedy Scheme
Statistical Scheme
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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
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The MEEC Scheme
slack
ß1
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An Important Property
The optimal expected energy consumption for
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Computing ßi
T1
T2
T3
T4
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Applying PACE

• PACE is a technique in which the execution speed
  is gradually increased as the task progresses

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The MEEC Scheme

• The ß 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

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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) 801520(f/1000)3

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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

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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

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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

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Why PACE Is Not Good in MEEC scheme?
PACE (under the assumption of unrestricted
continuous frequency)
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Results Synthetic Workload
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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

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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
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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

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Results - Automatic Target Recognition (ATR)
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Summary

• In this paper, we demonstrate and evaluate a new
  DVS scheme that aims to minimize the expected
  energy consumption in the system

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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

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• Thank you

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A Simple Example

• 3 tasks, the frame length is 14 time units
• For the CPU, c00, c11, fmin0, and fmax1