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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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slide1

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%
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
slide21
Energy-Efficient Task Allocation and Scheduling for Multi-Mode MPSoCs under Lifetime Reliability Constraint

Thank you for your attention !

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