Energy-Efficient Task Allocation and Scheduling for Multi-Mode MPSoCs under Lifetime Reliability Con...
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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 CU hk RE liable computing laboratory (CURE) The Chinese University of Hong Kong. Lifetime Reliability Becomes A Serious Concern. Infant

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Presenter lin huang lin huang and qiang xu cu hk re liable computing laboratory cure

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


Lifetime reliability becomes a serious concern

Lifetime Reliability Becomes A Serious Concern

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


Task allocation and scheduling

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

Problem Formulation

  • 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


Prior work

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

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

Feasible Solution Set

Systemwide

reliability threshold

G

F

D

E

A

Energy Consumption

B

C

O

Reliability


Feasible solution set1

X

Y

Feasible Solution Set

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


Feasible solution set identification

Feasible Solution Set Identification

  • 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


Feasible solution set identification1

Feasible Solution Set Identification

  • 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


Searching procedure for a single mode

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

  • Solution representation

    • (schedule order sequence; resource binding sequence)

    • Example: (0, 2, 1; P1, P1, P2)

  • Cost function


Searching procedure for a single mode1

Searching Procedure for a Single Mode

  • 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


Searching procedure for a single mode2

Searching Procedure for a Single Mode

  • 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


Multi mode combination

min

st.

or

Multi-Mode Combination

  • Optimization problem

    • Joint probability density function


Experimental setup

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

Case Study

  • 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%


Case study1

Case Study


Sensitivity analysis

Sensitivity Analysis

32%

39%

49%

17.27

12%

42%

27%


Extensive results

Extensive Results

29%

40%

49%

26-28%

energy

reduction


Conclusion

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


Presenter lin huang lin huang and qiang xu cu hk re liable computing laboratory cure

Energy-Efficient Task Allocation and Scheduling for Multi-Mode MPSoCs under Lifetime Reliability Constraint

Thank you for your attention !


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