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

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Feasible Solution Set Systemwide reliability threshold G F D E A Energy Consumption B C O Reliability

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. min st. or Multi-Mode Combination • Optimization problem • Joint probability density function

15. 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

16. 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%

17. Case Study

18. Sensitivity Analysis 32% 39% 49% 17.27 12% 42% 27%

19. Extensive Results 29% 40% 49% 26-28% energy reduction

20. 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

21. Energy-Efficient Task Allocation and Scheduling for Multi-Mode MPSoCs under Lifetime Reliability Constraint Thank you for your attention !