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Presenter: Lin Huang Lin Huang and Qiang Xu CU hk RE liable computing laboratory (CURE)

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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

Infant

mortality

Useful life

Wearout

90nm

130nm

180nm

Failure mechanisms

Electromigration

NBTI

TDDB

Failure rate

Time

[T. M. Mak]

< 7 year

~ 7 year

~ 10 year

- 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

- Given
- 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

MPSoC platform

Task graphs

- [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

- 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

Systemwide

reliability threshold

G

F

D

E

A

Energy Consumption

B

C

O

Reliability

X

Y

w

X – all the task schedules

Y – feasible solution set

u

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

- Static strategy

Systemwide

reliability threshold

G

F

Domain IV

D

E

A

Energy Consumption

Domain I

B

C

O

Domain II

Domain III

Reliability

Pareto optimal solution set

Feasible solution set

= {O,D,E}

= {O}

= {O,D}

The reached schedule is a feasible solution iff it is in the first or third domain of

all elements in feasible solution set

- Dynamic strategy
- Avoid heavy memory overhead

- Every newfound solution is processed according to …
- Rule 1 If the new solution is in domain I or III of ALL elements in set , it should be included into
- Rule 2 If the new solution is in domain II of ANY solution X in , we include the new solution into and eliminate X from
- Rule 3 If the new solution is in domain IV of ANY solution in , we ignore the new solution

Systemwide

Reliability Threshold

new

solution

original

updated

G

F

{}

C

{C}

{C}

O

{O}

D

E

{O,E}

E

{O}

A

Energy Consumption

{O,E}

{O,E,D}

D

{O,E,D}

{O,E,D}

F

{O,E,D}

B

{O,E,D}

B

C

O

{O,E,D}

A

{O,E,D}

{O,E,D}

G

{O,E,D}

Reliability

- 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

- Solution representation
- (schedule order sequence; resource binding sequence)
- Example: (0, 2, 1; P1, P1, P2)

- Cost function

- Solution representation
- (schedule order sequence; resource binding sequence)
- Example: (0, 2, 1; P1, P1, P2)

- Cost function

(0,2,1;P1,P1,P2;.6Vdd,.8Vdd,Vdd)

Resource binding

Solution space

(0,2,1;P1,P1,P2)

DVS

Schedule order

- Solution representation
- (schedule order sequence; resource binding sequence)
- Example: (0, 2, 1; P1, P1, P2)

- Cost function

Task schedule

Deadline

P1

0

1

P2

2

min

st.

or

- Optimization problem
- Joint probability density function

- 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

- Task graphs
- Occurrence probability
- (a) 0.3 (b) 0.3 (c) 0.4

- Reliability constraint
- The system reliability at 10 years is no less than 36.8%

32%

39%

49%

17.27

12%

42%

27%

29%

40%

49%

26-28%

energy

reduction

- 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

Thank you for your attention !