1. 1 A Run-Time Feedback Based Energy Estimation Model for Embedded Systems Selim Gürün Chandra Krintz Department of Computer Science U.C. Santa Barbara International Conference on Hardware/Software Codesign and System Synthesis (CODES-ISSS) Seoul, Korea October 22-25, 2006

2. CODES-ISSS’06 2 Power-Aware Execution: Big Picture Power-aware methods divide task execution into operations, and prepare an execution plan for each Operation: smallest user-visible unit of execution Typical operation: Rendering a scene, translating a sentence, calculating a shortest path in a map Need to know energy cost of each plan Knowing future energy cost of operations requires profiling them at run-time Identify Operations Profile at Runtime Predict Future Costs Develop Power-Aware Execution Strategy

3. CODES-ISSS’06 3 Outline Extant run-time power profiling techniques Power profiling methodologies for embedded computers Proposed model Overview Model construction Capturing system dynamics Evaluation Summary and Conclusion

4. CODES-ISSS’06 4 Run-Time Energy Profiling: Overview OS Interfaces like ACPI: + Provides simple API to battery voltage sensors + Ok for different hw. power levels - Very coarse - Not precise Execution Time: + Simple to measure + Fast and precise - Not correlated to power - Not suitable when hw. power levels change: DVS, sleep HPMs: + Fast access + Quite accurate - Architecture dependent - Not designed for power estimation --many events missing

5. CODES-ISSS’06 5 Run Time Energy Profiling: HPMs CPU counters provide unparalleled insight into program behavior Cache, TLB misses Instructions executed per cycle (IPC) How can we accurately gather program energy consumption by monitoring key parameters? Use HPMs as pseudo CPU component access counters: Energy Consumption = I Cache * a0 + D Cache * a1 + ALU * a2 +…

6. CODES-ISSS’06 6 Run Time Energy Profiling: HPMs CPU counters provide unparalleled insight into program behavior Cache, TLB misses Instructions executed per cycle (IPC) How can we accurately gather program energy consumption by monitoring key parameters? Useful but not enough: CPU consumes a portion of total energy; power-aware strategies need to know full picture. Fails when hardware changes its behavior: DVS, sleep states A different strategy needed! Use HPMs as pseudo CPU component access counters: Energy Consumption = I Cache * a0 + D Cache * a1 + ALU * a2 +…

7. CODES-ISSS’06 7 Proposed Energy Profiling Model Offline Analysis Continuous model improvement at run-time Fine-Grain Energy Estimation

8. CODES-ISSS’06 8 Case Study: Intel XScale on Stargate 32 bit XScale – 400MHz 64 MB RAM Runs Familiar Linux No Display Wireless 802.11 Compact Flash XScale Major HPM Events Inst/Data cache misses Data dependency stalls Inst/Data TLB misses Brach mispredicted Instruction executed SCL

9. CODES-ISSS’06 9 Constructing Model Are there any correlations between HPM values and full system power consumption? Absolutely! --but some challenges exist. Good correlation in memory/CPU subsystem High IPC -> CPU intensive application High cache misses/hits -> memory intensive application But I/O is the problem! Some heuristics possible, e.g. Low memory activity and low IPC -> possible I/O wait state Better to use software counters embedded into drivers

10. CODES-ISSS’06 10 Model Coefficients E = X1 a1 + X2 a2 + X3 a3 +… XI : Independent Variables aI : Coefficients Estimate coefficients using least squares linear regression (LSQ) Stable and simple Linearity assumption Only MajorAll related LSQ Model: Which variables? Efficient, clear Easier to understand Less accurate More accurate Run-time overhead Modeling difficulties due to variable dependencies

11. CODES-ISSS’06 11 Parameter Selection & Dependencies Hard to include all variables: Too many parameters clutter model Parameter dependencies  unstable parameter estimations E.g. Volume = a0 + a1 * pounds + a2 * grams Work-around is non-trivial; HPM characteristics e.g.: TLB miss  more CPU cycles & cache miss Memory Stall  Fewer instruction executed Multicollinearity!

12. CODES-ISSS’06 12 Run-Time Energy Estimation ComputationCommunication Simple Core Clock Cycles Data Stalls Core Clock Cycles Bytes Transmitted Bytes Received Complex Core Clock Cycles Instruction Cache Misses Instructions NDelivered Data Stalls ITLB Misses DTLB Misses Core Clock Cycles Bytes Transmitted Bytes Received Packets Transmitted Packets Received

13. CODES-ISSS’06 13 Run-Time Model Improvement Global coefficients Compute using off-line model Continuously update coefficients Improve using most recent data Gradually phase out previous measurements Recursive least squares with exponential decay Smaller decay factor-> more agile Global Coefficients Measure Power Update with RLS Model Parameters: Decay factor Update period Measurement error

14. CODES-ISSS’06 14 Feedback Source: DS 2760 Measures current flow in and out of battery Internally: A small A/D converter attached to a high precision internal resistor Pros/Cons: + Highly Available e.g. iPAQs, sensor network gateways, cell phones - Not precise enough for monitoring task energy consumption 0.25 mAh error in each reading - Slow, one-wire serial interface

15. CODES-ISSS’06 15 Stargate and Our Evaluation Bench PowerTool VPerfmon VPMon SCL High-precision Data Acquisition Device Programmable Power Supply

16. CODES-ISSS’06 16 Methodology Collect energy consumption every so often Every 10 million instructions ( a so-called interval) Validate model accuracy on imprecise measurement data Inject uniformly distributed random error Evaluate various precision (error) levels: 1X – 8X Predict energy consumption of each interval Continuously improve model parameters every 10M * K intervals Use a large group of workload Computational benchmarks Computational + communication oriented benchmarks

17. CODES-ISSS’06 17 Static vs. Adaptive Models

18. CODES-ISSS’06 18 Average Error Rates Interval Size 1X2X4X8XBest 10053.3%29.9%14.5%16.9%3.8% 20068.8%24.1%12.9%7.3%2.7% 40033.0%22.0%9.1%7.7%2.8% Error rates and Interval sizes –Simple Model Measurement Precision

19. CODES-ISSS’06 19 Average Error Rates-Complex Model Interval Size 8X Best 10016.9%28.1%3.8%4.3% 2007.3%33.3%2.7%3.8% 4007.7%24.0%2.8%4.1% Measurement imprecision reduce complex model quality more than the simple one! Simple Model

20. CODES-ISSS’06 20 Related Work High-End CPU Power Models Define CPU component access rate using HPM access heuristics OS calls power consumption as a function of IPC Embedded CPU Power Models Five HPM counters for XScale Also evaluated memory model Memory models UltraSparc memory subsystem All above are static models Power profiling setups Powerscope

21. CODES-ISSS’06 21 Summary & Conclusions Our Goal: An accurate, efficient run-time power profiling system Hardware counters are key Define software counters for I/O Smart battery monitors expose dynamics in power behavior We propose a hybrid system that combine both Lessons learned Dynamic models are much better than static ones in power modeling Models should decay old measurements conservatively when measurement errors are present Measurement errors in the presence of multicollinearity can be deadly

22. CODES-ISSS’06 22 Backup Slides

23. CODES-ISSS’06 23 Decay Factor vs. Accuracy

24. CODES-ISSS’06 24 Execution Cost

25. CODES-ISSS’06 25 Benefit from an Offline Profiler

26. CODES-ISSS’06 26 Power-Aware Execution: Case Study Speech Recognition Execution Plans src: Flinn’01 Baseline Local Reduced Remote Reduced Requires run-time power prediction of different execution plans!