Post on 08-Feb-2016
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Customer-Aware Task Allocation and Scheduling for Multi-Mode MPSoCs
Lin Huang, Rong Ye and Qiang XuCHhk REliable computing laboratory (CURE)
The Chinese University of Hong Kong
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TAS and Execution Modes• Task Allocation and Scheduling
• Multi-Mode MPSoCs (multiple execution modes)• Communication service• Audio/Video player• Digital camera…
P1 P2
MPSoC PlatformT0
T1
T2
T3
T4
TaskGraph
Allocation &Scheduling
T0
P1
P2 T1
T2
T3
T4PeriodicalSchedule
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Personalized TAS• Prior Works [Huang etc., DATE’09, DATE’10]
• TAS solutions are generated at design stage• A unified task schedule for each execution mode is
constructed for all the products
• Usage Strategy Deviation• The products, bought by different end users, experience
different life stories.• Personalized TAS solution for each individual product
can be more energy-efficient and/or reliable
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Motivational Example• Consider
• A simple MPSoC product with 3 execution modes and 2 processor cores• 10,000 sample products
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Problem Formulation• Problem 1 [Design Stage]
• Given– q execution modes and a directed acyclic task graph for each mode;– The joint probability density function;– A platform-based MPSoC embedded system;– Execution time table;– Power consumption table;– The target service life and the corresponding reliability requirement.
• To determine a periodical task schedule for each execution mode, such that the expected energy consumption over all products is minimized under the performance and reliability constraints
• Problem 2 [Online Adjustment]• Given
– Interval length;– Usage strategy of a specific interval;– Task mapping flexibility constraints.
• To achieve the same optimization as Problem 1
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Proposed TAS at Design Stage• Simulated annealing-based algorithm to minimize the
expected energy consumption over all the products• Solution representation
• Two kinds of moves• M1: Insert a task in the front of its sink, if no precedunce constraint between them• M2: Change the resource assignment of a task
• Cost function
Task Graph Task Schedule Zone Representation
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Proposed Online Adjustment• Overall flow
• Resort to similar technique as design stage;• The main difference stays in particularly in the cost function.• Since aging effect is a slow process, online adjustment is performed
at regular intervals in range of days or months as a special task.• Analytical model
• A forgetful scheme to infer future usage strategy
• System reliability is given by
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Experimental Results• Without mapping constraints
Initial Solution Online Adjustment
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Experimental Results• With mapping constraints
Online Adjustment (25% tasks with constraints)
Online Adjustment(50% tasks with constraints)
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Conclusion• Customer-aware TAS on multi-mode MPSoCs• Two phases of proposed approach
• Simulated annealing-based algorithm at design stage• Usage-specific online adjustment
• Experimental results • Based on hypothetical MPSoCs with various task graphs;• Show the capability to significantly increase the lifetime reliability
and energy reduction of MPSoC products.
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