1. 1 Energy Metering for Free: Augmenting Switching Regulators for Real-Time Monitoring Prabal Dutta †, Mark Feldmeier ‡, Joseph Paradiso ‡, and David Culler † Computer Science Division † University of California, Berkeley {prabal,culler}@cs.berkeley.edu The Media Laboratory ‡ Massachusetts Institute of Technology {carboxyl,joep}@mit.edu

2. 2 Energy is a critical resource in this domain… So, why don’t more publications provide empirical evidence of a change in energy usage in situ or at scale?

3. 3 Current energy metering techniques are inadequate cumbersome, expensive, not distributed, not scalable, not embedded cumbersome, expensive, not distributed, not scalable, not embedded, low resolution, low responsiveness, high quiescent power low responsiveness, high cost, high quiescent power DS2438 ADM1191 BQ2019 BQ27500 [Jiang07]

4. 4 How simply can energy metering be performed? If your platform has a PFM switching regulator… (increasingly, many do) very simply: iCount energy meter design The network-wide cost of the CSMA overhearing problem Energy division between route-through and local traffic Energy benefits of batching or compressing data

5. 5 This simple design works surprisingly well MAX1724 Our implementation

6. 6 Outline Introduction How does it work? How well does it work? How much does it cost? What are its limitations? How could it be used?

7. 7 How does it work? Source: Maxim Semiconductor C in LxLx V in C out V out R load i LX Energize Transfer Monitor S1S1 S2S2 V LX E=½Li 2 PFM Regulator

8. 8 The key insight: each regulator cycle transfers a fixed amount of energy to the load ΔE=½Li 2 P=ΔE/ΔtP=ΔE/Δt

9. 9 Outline Introduction How does it work? How well does it work? –Range –Accuracy –Resolution –Responsiveness –Precision –Stability How much does it cost? What are its limitations? How could it be used?

10. 10 A typical mote-class system exhibits a 10000:1 dynamic range in current draw (5 µA to 50 mA) iCount offers a dynamic range exceeding 100000:1

11. 11 iCount exhibits less than ±20% error over five decades of current draw Common Operating Points iCount exhibits lower error over mote operating range

12. 12 A Telos mote uses about 20 µJ per second when sleeping iCount resolves less than 1 µJ

13. 13 A mote’s energy-consuming events can occur in as little as 100 µs [Jiang07] iCount responds in less than 125 µs to sudden changes in current draw

14. 14 iCount is precise over short periods (2 sec) so one or two samples is enough to estimate the instantaneous current All samples fall within ±2% of the median

15. 15 iCount is stable over long periods (1 week) All samples fall within ±1% of the median

16. 16 Outline Introduction How does it work? How well does it work? How much does it cost? –Hardware –Software –Energy What are its limitations? How could it be used?

17. 17 Hardware costs include a wire and a microcontroller counter “wire” Counter HydroSolar Node (v2)

18. 18 Software costs include initializing hardware and handling load-dependent counter overflows Control Access (15 µs) Overflow Initialization

19. 19 Energy costs include switching gate capacitors and handling load-dependent counter overflows 1% 0.01%

20. 20 Outline Introduction How does it work? How well does it work? How much does it cost? What are its limitations? –Efficiency –Voltage dependence –Calibration How could it be used?

21. 21 Regulator inefficiency can make battery gas gauging challenging

22. 22 Input voltage dependence requires calibration (not fundamental, but an artifact of the MAX1724)

23. 23 Calibration is required either at manufacturing or at run-time Calibration Reg

24. 24 Estimating per-component current draws from the aggregate RGBΔEΔEΔtΔt 0008241024 100123361024 010188061024 110304341024 001149401024 101264321024 011328041024 111442471024 Regression Log X = [ones(size(R)) R G B]; p = dE./ dt; i = p / 3; a = X\i; y = [dt transpose(a)];

25. 25 Conclusion iCount - simple, functional, research-enabling research

26. 26 Future directions and enabled research Hardware profiling – estimating per-subsystem power draw Model validation – do theory and practice agree in practice and at scale? Real-time current metering – measuring the instantaneous current draw Software energy profiling – where have all the joules gone? Runtime adaptation – equal-energy scheduling by the operating system Gas gauging – estimating remaining battery energy Voltage independence – ensuring a cycle delivers the same energy independent of input voltage

27. 27 Questions?

28. 28 Performance summary Performance MetriciCount Range1 µA – 100 mA Accuracy±20% Resolution0.1 µJ – 0.5 µJ Read latency15 µs Power overhead1% - 0.01% Responsiveness< 125 µs Precision±1.5% (over 2 secs) Stability±1% (over 1 week)* * Frequency averaged over 1 second

29. 29 Current energy metering techniques are inadequate Metric Battery Fuel Gauge [DS2438/ADM1191/ AC Metering [ADE7753/MCP3906] SPOT [Jiang07] Range45000:1 Accuracy±3% (0-9 µA) Resolution< 1 µA Read latencySPI/- Power overhead4-7 mA Responsiveness? Precision Stability