Customer-Aware Task Allocation and
Scheduling for Multi-Mode MPSoCs
Lin Huang, Rong Ye and Qiang Xu
CHhk REliable computing laboratory (CURE)
The Chinese University of Hong Kong
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TAS and Execution Modes
•
Task Allocation and Scheduling
T0
Task
Allocation &
Graph T
Scheduling
1
T2
T3
T4
P1
P2
•
T2
T0 T1
T3
T4
MPSoC Platform
P1
P2
Periodical
Schedule
Multi-Mode MPSoCs (multiple execution modes)
•
•
•
Communication service
Audio/Video player
Digital camera…
2
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
3
Motivational Example
•
Consider
•
•
A simple MPSoC product with 3 execution modes and 2 processor cores
10,000 sample products
4
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
Task Graph
•
Zone Representation
Two kinds of moves
•
•
•
Task Schedule
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
m
tL  j
sys
R (tL )  exp( (
) )
j 1 ( s )
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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
y  (1   )  yu  (1   )    yu 1 
•
  u 1  y1
System reliability is given by
u
tL  u  tI
tI

; yu , su )  exp( (

) j)
 j ( s)
j 1
1  j ( s )
m
sys
R (tL ; y, s | y1 , s1;
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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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