1. Power-aware scheduling Jan Madsen Informatics and Mathematical Modelling Technical University of Denmark Richard Petersens Plads, Building 321 DK2800 Lyngby, Denmark Jan@imm.dtu.dk

2. Mission critical embedded systems • Based on work by • J. Liu, • P.H. Chou, • N. Bagherzadeh, • F. Kurdahi • University of California, Irvine • CODES’01 & DAC’01 Jan Madsen

3. Mars Rover – Mission • Perform experiments • Autonomous mobile vehicle • Alpha proton X-ray spectrometer • Imaging • Travel between different target locations Jan Madsen

4. Mars Rover – Conditions • Surface temperature [-40 oC; -80 oC] • Communication ~ 11 minute • No real-time control • Supervised autonomous control Jan Madsen

5. Mars Rover - System composition • CPU • 3 images per day • Motors • 60 cm per min • Hazard detection • Heaters • -80 oC requires motors to be heathed Jan Madsen

6. Mars Rover – Power? • Power sources • Battery (non-rechargeable) • Solar panel (free) • Power consumers • Digital: imaging, communication, control • Mechanical: driving, steering • Thermal: heating motors in the low-temperature environment Jan Madsen

7. System-level power manager • Amdalhs’ law applies to power • Power savings of a component is scaled to its contribution to power usage of the whole system • If a component draws 2% of the power in a system, a 50% power reduction amounts to 1% saving to the system • The power manager must consider all power consumers in the entire system and identify the major power consumers Jan Madsen

8. System-level power manager • System-level power consumers • (Digital) computation domain • Processors, memory, I/O, ASIC • Non-computation domains • Mechanical: motors • Thermal: heaters • Major power consumers: mechanical and thermal Jan Madsen

9. Power-aware vs. low-power • Low-power • Minimize power usage • Just enough power to meet performance requirement • No distinction between costly power and free power • Component-level power managers • Power-aware • Best use of available power • Minimize power usage with low power budget • Deliver high performance with high power budget • Distinguish different models of power sources • Battery, solar, nuclear, etc. • Track variant power availability • System-level power managers Jan Madsen

10. Low-power scheduling • Shutting down subsystems • Variable-voltage processor scheduling • Limited applicability to power-aware designs • Timing constraints are not strongly guaranteed • Power usage is handled as a by-product • No tracking to power availability • No distinction to different energy sources Jan Madsen

11. p1 r1 r1 p2 r1 p3 r1 idle r1 idle p1 p2 p3 r1 idle Low-power scheduling - Example r1 r1 r1 Jan Madsen

12. Power-aware scheduling • Min/max timing constraints on tasks • Min timing constraint • Subsumes precedence as special cases • Max timing constraint • Subsumes deadline as special cases • Min/max power constraints on the system • Max power constraint • Total power budget from the available sources • Hard constraint, must be guaranteed • Min power • Free power (solar), minimize power jitter • soft constraint, best effort Jan Madsen

13. Vertices V: tasks d(v), execution delay p(v), power consumption r(v), resource mapping Edges E: timing constraints Forward edge: min constraint Backward edge: max constraint Constraint graph G(V, E) Jan Madsen

14. Schedule  Time assignments to tasks Finish time  Timing-valid schedule Timing constraints satisfied No resource conflict Constraint graph G(V, E) Jan Madsen

15. Time view Bins – tasks Horizontal axis – start time, duration Vertical axis – power Tracks – parallel resources Power view Power profile Power constraints Power properties Spikes, gaps Energy cost Utilization Power-aware Gantt chart Jan Madsen

16. Mars Rover - Exercise Jan Madsen

17. Power sources & tasks Duration (sec.) Power @ -40 oC Power @ -60 oC Power @ -80 oC Solar panel 17 14 11 Battery pack 8 max 8 max 8 max CPU Constant 2 3 4 Heating two motors 5 8 10 12 Driving 10 8 11 14 Steering 5 4 6 8 Hazard detection 10 3 4 5 Mars Rover - Exercise Jan Madsen

18. Hd St Dr HW12 HW34 HW56 HS12 HS34 CPU Power 9 9 16 16 16 16 16 12 18 18 9 9 12 18 18 Mars Rover - Solution Worst case at –80 oC Jan Madsen

19. Power profile P(t) System-level power consumption curve Power constraints Max power constraint Pmax Power Spike: max power constraint violation Min power constraint Pmin Power Gap: min power constraint violation Power-validity A timing-valid schedule with no power spikes Enforce max power budget Min power utilization (Pmin) Energy utilization from free sources Energy cost Ec(Pmin) Energy drawn from expensive (non-free) sources Power-aware trade-off Performance  vs. Energy cost Ec(Pmin) Power properties Jan Madsen

20. Pmax P(t) Pmin (11 x 75) – (2 x 2 x 10) (11 x 75) 5x25+5x1+10x7+5x1+10x7 75 Mars Rover – Power profile 20 10 (Pmin) = = 95.2 % Ec(Pmin) = = 3.4 Jan Madsen

21. Mars Rover – the real thing! • Timing constraints • Three cases w/ different power constraints • Max power: • solar + 10W • Min power • solar, free • Best: 14.9W • Typical: 12W • Worst: 9W Jan Madsen

22. Best case Fast, low cost Typical case Slower, increased cost Worst case Slower, high cost Same as the existing serial schedule Scheduling results Jan Madsen

23. Existing low-power schedule Low performance Low energy cost Under-utilized free solar power Does not track power sources Full serialization by hand-crafting Power-aware schedules High performance High energy cost Improved utilization of solar power Tracks available power from different sources Fully constraint-driven by an automated design tool Comparisons to schedules Jan Madsen

24. Scenario Mission: travel to a target 48 steps away Existing low-power schedule Fixed slow speed Low energy cost in each phase, but high energy cost in worst case Low performance, high energy cost 3 phases: best, typical, worst, 10 min each Power-aware schedules Accelerated speed by tracking available power Finish earlier before working in the worst case High performance, low energy cost Comparisons in a scenario Jan Madsen

25. Conclusion • Power-aware design • Different from low-power • Deliver high performance by tracking power sources • Power-aware schedulers • Incremental scheduling by constraint classification • Potentials on performance speedup and energy saving • System-level design tools • Power manager for the entire system • Aggressive design space exploration Jan Madsen

26. Incremental scheduling (1) • (1) Timing scheduling • Topological traversal of the constraint graph • Selective serialize tasks that share the same resource • Prohibit positive cycles • Proven to find a timing-valid schedule Jan Madsen

27. Incremental scheduling (2) • (2) Max power scheduling • Begin with a timing-valid schedule from (1) • Enforce max power constraint • Reorder tasks to eliminate power spikes • Redo (1) for timing violation • Heuristics applied Jan Madsen

28. Incremental scheduling (3) • (3) Min power scheduling • Begin with a power-valid schedule from (2) • Reorder tasks to reduce power gaps in best-effort • Deliver same performance with less energy cost • Heuristics applied • Results applicable to different constraints Jan Madsen