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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