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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 1 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? 2 Current energy metering techniques are inadequate cumbersome, expensive, not distributed, not scalable, not embedded DS2438 ADM1191 BQ2019 BQ27500 low resolution, low responsiveness, high quiescent power cumbersome, expensive, not distributed, not scalable, not embedded, [Jiang07] low responsiveness, high cost, high quiescent power 3 How simply can energy metering be performed? If your platform has a PFM switching regulator…very simply: (increasingly, many do) 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 4 This simple design works surprisingly well MAX1724 Our implementation 5 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? 6 How does it work? E=½Li2 Lx Vin iLX Vin Vout VLX Cin PFMS2 Regulator S1 Cout Rload Energize Transfer Monitor Source: Maxim Semiconductor 7 The key insight: each regulator cycle transfers a fixed amount of energy to the load 2 ΔE=½Li P=ΔE/Δt 8 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? 9 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 10 iCount exhibits less than ±20% error over five decades of current draw Common Operating Points iCount exhibits lower error over mote operating range 11 A Telos mote uses about 20 µJ per second when sleeping iCount resolves less than 1 µJ 12 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 13 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 14 iCount is stable over long periods (1 week) All samples fall within ±1% of the median 15 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? 16 Hardware costs include a wire and a microcontroller counter Counter “wire” HydroSolar Node (v2) 17 Software costs include initializing hardware and handling load-dependent counter overflows Initialization Overflow Control Access (15 µs) 18 Energy costs include switching gate capacitors and handling load-dependent counter overflows 0.01% 1% 19 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? 20 Regulator inefficiency can make battery gas gauging challenging 21 Input voltage dependence requires calibration (not fundamental, but an artifact of the MAX1724) 22 Calibration is required either at manufacturing or at run-time Reg Calibration 23 Estimating per-component current draws from the aggregate R G B ΔE Δt 0 0 0 824 1024 1 0 0 12336 1024 0 1 0 18806 1024 1 1 0 30434 1024 0 0 1 14940 1024 1 0 1 26432 1024 0 1 1 32804 1024 1 1 1 44247 1024 X p i a y = = = = = [ones(size(R)) R G B]; dE ./ dt; p / 3; X\i; [dt transpose(a)]; 24 Conclusion iCount - simple, functional, research-enabling research 25 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 26 Questions? 27 Performance summary Performance Metric iCount Range 1 µA – 100 mA Accuracy ±20% Resolution 0.1 µJ – 0.5 µJ Read latency 15 µs Power overhead 1% - 0.01% Responsiveness < 125 µs Precision ±1.5% (over 2 secs) Stability ±1% (over 1 week)* * Frequency averaged over 1 second 28 Current energy metering techniques are inadequate Metric Battery Fuel Gauge [DS2438/ADM1191/ AC Metering [ADE7753/MCP3906] SPOT [Jiang07] Range 45000:1 Accuracy ±3% (0-9 µA) Resolution < 1 µA Read latency SPI/- Power overhead 4-7 mA Responsiveness ? Precision Stability 29