Energy-Efficient Task Allocation and
Scheduling for Multi-Mode MPSoCs
under Lifetime Reliability Constraint
Presenter: Lin Huang
Lin Huang and Qiang Xu
CUhk REliable computing laboratory (CURE)
The Chinese University of Hong Kong
hk
l i a b l e C o m pu t i n g L a b o r a t o r y
Lifetime Reliability Becomes A Serious Concern
Infant
mortality
Useful life
Wearout
Failure rate
90nm 130nm 180nm
Failure mechanisms
Electromigration
NBTI
TDDB
Time
[T. M. Mak]
< 7 year
~ 7 year ~ 10 year
Task Allocation and Scheduling
 Multiprocessor system-on-chip (MPSoC) platform
 Energy-efficient task allocation and scheduling
 Multi-mode MPSoC
 For instance, a modern smart phone can serve as






MP3 player
Game console
Digital camera
Video decoder
GPS navigation
…
MPSoC platform
It is essential to explicitly consider lifetime reliability issue in
energy-efficient embedded system designs
Problem Formulation
 Given
0
0
1
2
1
2
0
3
1
3
5
4
4
2
5
6
(a)
Task graphs
(b)
MPSoC platform
(c)
 and the joint probability density function
 Determine a task schedule for each execution mode such that
 The expected energy consumption is minimized
 The performance and reliability constraints are met
Prior Work
 [Huang&Xu DATE’09] explicitly takes the lifetime reliability
into account during task allocation and scheduling
 Energy consumption issues are not considered
 Focus on single execution mode only
 Maximize the expected service life under performance constraint
Agenda
 Introduction and motivation
 Problem formulation
 Proposed algorithm for multi-mode embedded systems
 Task schedule generation for each execution mode
 Multi-mode combination
 Experimental results
 Conclusion
Feasible Solution Set
Energy Consumption
Systemwide
reliability threshold
G
F
E
A
B C
O
Reliability
D
Feasible Solution Set
X
w
X – all the task schedules
Y
u
Y – feasible solution set
v
Internal stability Given two solutions u,v ∈ Y, if u consumes more energy than v,
it must have higher lifetime reliability at the target service life, and vice versa
External stability For any solution w ∈ X \ Y, there exists at least one solution u
∈ Y such that u consumes less energy and have higher lifetime reliability than w
Feasible Solution Set Identification
Energy Consumption
 Static strategy
Systemwide
reliability threshold
G
F
Domain IV
E
A
D
Domain I
B C
O
Domain II
Domain III
Reliability
Feasible solution set
= {O,D,E}
{O}
{O,D}
Pareto optimal solution set
The reached schedule is a feasible solution iff it is in the first or third domain of
all elements in feasible solution set
Feasible Solution Set Identification
 Dynamic strategy
 Avoid heavy memory overhead
Energy Consumption
 Every newfound solution is processed according to …
 Rule 1 If the Systemwide
new solution is in domain I or III of ALL elements in
Reliability Threshold
set , it should be included into
new
original
solution
G solution isF in domain II of
 Rule 2 If the new
ANY solution
X inupdated
, we
{C}
include the new solution into
and eliminate{}X from C
{C}
O
 Rule 3 If the new solution is in domain
, {O}
we
D IV of ANY solution in
E
{O,E}
{O}
A ignore the new solution E
B C
O
Reliability
{O,E}
{O,E,D}
{O,E,D}
{O,E,D}
{O,E,D}
D
F
B
A
G
{O,E,D}
{O,E,D}
{O,E,D}
{O,E,D}
{O,E,D}
Searching Procedure for a Single Mode
 Modified simulated annealing
 Classic SA keeps the current solution and the best one so far
 Modified SA keeps a possible solution set
 Static strategy
 Dynamic strategy
0
 Solution representation
0
1
1
2
 (schedule order sequence; resource binding sequence)
2
3
 Example: (0, 2, 1; P1, P1, P2)
 Cost function

3
5
4
4
0
1
2
5
6
(a)
(b)
(c)
Searching Procedure for a Single Mode
0
 Solution representation
0
1
1
2
 (schedule order sequence; resource
binding 3sequence)
2
 Example: (0, 2, 1; P1, P1, P2)
 Cost function

3
5
4
Resource binding
4
(0,2,1;P1,P1,P2;.6V(a)
dd,.8Vdd,Vdd)
(0,2,1;P1,P1,P2)
Schedule order
0
1
2
5
6
(b)
Solution space
(c)
Searching Procedure for a Single Mode
0
 Solution representation
0
1
1
2
 (schedule order sequence; resource
binding 3sequence)
2
 Example: (0, 2, 1; P1, P1, P2)
 Cost function
3

5
4
4
Deadline
P2
2
6
Task schedule
0
1
5
(a)
P1
0
1
2
(b)
(c)
Multi-Mode Combination
 Optimization problem
min
st.
or
 Joint probability density function
Experimental Setup
 Task graphs are generated by TGFF
 The power consumption values are randomly generated, while
the range is set according to state-of-the-art technology
 Well-studied electromigration failure model
 The proposed model is applicable for the combination of multiple
failure mechanisms
 Baseline solution
 We first build a schedule to shorten schedule length and reduce energy
consumption with list scheduling
 We then attempt to meet the reliability constraint in a greedy manner
 Single mode method
Case Study
 Task graphs
0
0
1
2
1
2
0
3
1
3
5
4
4
2
5
6
(a)
 Occurrence probability
(b)
(c)
 (a) 0.3 (b) 0.3 (c) 0.4
 Reliability constraint
 The system reliability at 10 years is no less than 36.8%
Case Study
1
2
3
4
5
6
7
39.16
36.70
34.91
34.18
27.92
17.05
11.80
23.179
22.992
22.370
21.312
20.503
20.061
19.253
Resource
Binding
Sequence
(0,0,1,0,1,1)
(1,0,1,0,1,0)
(0,0,1,1,1,1)
(1,0,1,0,1,1)
(1,0,1,1,1,1)
(1,1,1,0,1,1)
(1,1,1,1,1,1)
1
2
3
(b)
4
5
6
65.09
59.19
56.94
47.54
45.49
36.51
15.437
15.358
14.921
14.910
14.488
14.477
(1,0,1,1,1,0,0)
(1,1,1,1,0,0,0)
(1,0,1,1,0,0,0)
(1,0,0,1,0,0,0)
(1,0,1,0,0,0,0)
(1,0,0,0,0,0,0)
#
(a)
Ri (%)
Ei
Approach
Baseline
Single
Mode
Multi
Mode
(c)
graph Ri (%)
(a)
(b)
42.71
(c)
41.98
(a)
39.16
(b)
45.49
(c)
39.77
(a)
11.80
(b)
47.54
(c)
27.10
#
Ri (%)
Ei
1
2
3
4
5
44.05
41.98
39.77
35.33
27.10
23.036
22.559
19.889
17.034
16.556
Ei
15.008
22.559
23.179
14.488
19.889
19.253
14.910
16.556
E[E]
-
19.255
16.875
Resource
Binding
Sequence
(1,1,0)
(0,1,0)
(1,0,1)
(1,0,0)
(0,0,0)
Sensitivity Analysis
32%
39%
49%
17.27
12%
27%
42%
Extensive Results
29%
26-28%
energy
reduction
40%
49%
Conclusion
 Lifetime reliability has become a serious concern nowadays
 Today’s complex embedded system typically have multiple
execution modes
 We propose novel task allocation and scheduling algorithm
 Objective: to minimize the expected energy consumption under
performance and reliability constraints
 We first identify a set of “good” schedules for each execution mode
 We then introduce novel techniques to obtain an optimal combination
 The effectiveness has been demonstrated by experiments
Energy-Efficient Task Allocation and Scheduling for
Multi-Mode MPSoCs under Lifetime Reliability Constraint
Thank you for your attention !