1. courseware 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. SoC-MOBINET coursewareJan Madsen 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

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

4. SoC-MOBINET coursewareJan Madsen Mars Rover – Conditions  Surface temperature [-40 o C; -80 o C]  Communication ~ 11 minute  No real-time control  Supervised autonomous control

5. SoC-MOBINET coursewareJan Madsen Mars Rover - System composition  CPU  3 images per day  Motors  60 cm per min  Hazard detection  Heaters  -80 o C requires motors to be heathed

6. SoC-MOBINET coursewareJan Madsen 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

7. SoC-MOBINET coursewareJan Madsen 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

8. SoC-MOBINET coursewareJan Madsen 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

9. SoC-MOBINET coursewareJan Madsen 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

10. SoC-MOBINET coursewareJan Madsen 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

11. SoC-MOBINET coursewareJan Madsen Low-power scheduling - Example p3p3 p2p2 p1p1 r1r1 r1r1 r1r1 r1r1 p3p3 p2p2 p1p1 r1r1 r1r1 r 1 idle

12. SoC-MOBINET coursewareJan Madsen 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

13. SoC-MOBINET coursewareJan Madsen Constraint graph G(V, E)  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

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

15. SoC-MOBINET coursewareJan Madsen Power-aware Gantt chart  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

16. SoC-MOBINET coursewareJan Madsen Mars Rover - Exercise

17. SoC-MOBINET coursewareJan Madsen Mars Rover - Exercise Power sources & tasks Duration (sec.) Power @ -40 o CPower @ -60 o CPower @ -80 o C Solar panel 171411 Battery pack 8 max CPUConstant234 Heating two motors581012 Driving1081114 Steering5468 Hazard detection10345

18. SoC-MOBINET coursewareJan Madsen Mars Rover - Solution Hd St Dr HW12 HW34 HW56 HS12 HS34 CPU Power9 916 1218 991218 Worst case at –80 o C

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

20. SoC-MOBINET coursewareJan Madsen Mars Rover – Power profile P  (t) 10 20 P max P min   (P min ) = = 95.2 % (11 x 75) – (2 x 2 x 10) (11 x 75) Ec  (P min ) = 5x25+5x1+10x7+5x1+10x7 75 = 3.4

21. SoC-MOBINET coursewareJan Madsen 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

22. SoC-MOBINET coursewareJan Madsen Scheduling results  Best case  Fast, low cost  Typical case  Slower, increased cost  Worst case  Slower, high cost  Same as the existing serial schedule

23. SoC-MOBINET coursewareJan Madsen Comparisons to schedules  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

24. SoC-MOBINET coursewareJan Madsen Comparisons in a scenario  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

25. SoC-MOBINET coursewareJan Madsen 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

26. SoC-MOBINET coursewareJan Madsen 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

27. SoC-MOBINET coursewareJan Madsen 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

28. SoC-MOBINET coursewareJan Madsen 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