Obtaining an accurate estimate of power consumption is essential when designing a system’s power supplies, voltage regulators, heat sink, and cooling system, and is crucial in developing an appropriate power budget for the entire system.

Obtaining an accurate estimate of power consumption is essential when designing a system’s power supplies, voltage regulators, heat sink, and cooling system, and is crucial in developing an appropriate power budget for the entire system.
FPGA Power Management and Modeling
Techniques
WP-01044-2.0
White Paper
This white paper discusses the major challenges associated with accurately predicting
power consumption in FPGAs, namely, obtaining accurate signal activities, static
power modeling, and dynamic power modeling, as well as how Altera addresses
these challenges through the PowerPlay early power estimator and the Quartus® II
PowerPlay power analyzer. This paper also presents the accuracy of the model by
comparing predicted power consumption with actual silicon measurements using an
extensive suite of real-world customer designs. Using these best-in-class power
analysis tools, a designer can model the power consumption of their design to within,
on average, ±10% accuracy when used with accurate design information.
Introduction
As designs get larger and add more system functions implemented on FPGAs, and as
the advanced silicon process technology moves into smaller geometries, power
consumption is increasingly a concern for today’s FPGAs. When designing a printed
circuit board (PCB), the power consumed by a device determines not only the power
supply, but also the cooling system required for the device. Therefore, obtaining an
accurate estimate of power consumption is essential to design a system’s power
supplies, voltage regulators, heat sink, and cooling system, and is crucial in
developing an appropriate power budget for the entire system.
Factors Contributing to Accurate Power Estimates
Obtaining an accurate power estimate relies on two important factors: accurate signal
activities and accurate power models. Power models define the power characteristics
of the device, and the signal activities define the behavioral characteristics of each
signal in the design. While the power models are provided by FPGA vendors, the
designer must provide accurate signal activities to obtain the best possible estimate of
overall power consumption.
Signal Activities
A good power analysis tool should provide a flexible framework for specifying signal
activities. Using representative signal activity data during power analysis is
important, as inaccurate signal activity data is the largest source of power estimation
error.
Altera’s PowerPlay power analysis tools use the following sources to provide
information on signal activity:
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December 2010
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Simulation results
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User-entered node, entity, and clock assignments
© 2010 Altera Corporation. All rights reserved. ALTERA, ARRIA, CYCLONE, HARDCOPY, MAX, MEGACORE, NIOS,
QUARTUS and STRATIX are Reg. U.S. Pat. & Tm. Off. and/or trademarks of Altera Corporation in the U.S. and other countries.
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accordance with Altera’s standard warranty, but reserves the right to make changes to any products and services at any time
without notice. Altera assumes no responsibility or liability arising out of the application or use of any information, product, or
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Power Models
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User-entered default toggle rate assignment
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Vectorless estimation
For some Altera® device families, the PowerPlay power analyzer automatically
estimates signal activity on nodes that have incomplete simulation or user-entered
signal-activity data. Vectorless estimation statistically estimates the signal activity of a
node based on the signal activities of all nodes feeding that node, and on the actual
logic function that is implemented by the node. In addition to combinational logic
functions, vectorless estimation also estimates signal activity data for nodes that are
the outputs of multipliers, memories, and other blocks. Vectorless estimation is
generally accurate for combinational nodes, but not for registered nodes. Therefore,
simulation data for at least the registered nodes and I/O nodes is needed to ensure
accuracy.
Power Models
The other critical component of accurate power analysis is the power models that the
power analysis tools use to characterize power consumption in the device. Device
power consumption is comprised of static power and dynamic power, and each
should be modeled separately by the power analysis tools. When combined with
representative signal activity data, this approach provides the best and most accurate
estimate of the design’s overall power consumption.
Static Power Models
Static power is the power consumed by a device due to leakage currents when in
quiescent state, that is, with no activity or switching in the design. The amount of
leakage current varies with die size, junction temperature, and process variation.
Static power can be modeled using a set of exponential equations that vary with
BT
junction temperature (Tj): P static = Ae j + C .
The dependence of static power on process variation is modeled using separate static
power equations for “typical” and “worst-case” devices. Every device shipped is
guaranteed to have static power within the worst-case curve, while the typical curve
represents the more likely static power consumption of a typical device, as shown in
Figure 1.
Figure 1. Static Power Distribution Curve
Number of Devices
“Typical” Static Power
“Worst-Case”
Static Power
Static Power at 85°C (W)
FPGA Power Management and Modeling Techniques
December 2010 Altera Corporation
Power Models
Page 3
Dynamic Power Models
Dynamic power is the additional power consumed through device operation caused
by signals toggling and capacitive loads charging and discharging. As shown in
Figure 2, the main variables affecting dynamic power are capacitance charging,
supply voltage, and clock frequency.
Figure 2. Variables Affecting Dynamic Power
ª
º
Pdynamic = « 1 CV 2 + QShortCircuit V » f ˜ activity
¬2
¼
Capacitance
Charging
Short Circuit Charge Percent of Circuit that
During Switching
Switches Each Cycle
It is important to use software tools that accurately predict the dynamic power
consumption of a design. Unsophisticated power analysis tools simply model each
circuit as a lumped capacitance. In contrast, the PowerPlay power analysis tools use
two very detailed dynamic power models: simulation-based power models and
empirical power models.
Simulation-Based Dynamic Power Models
Simulation-based dynamic power models are used to model blocks on the device that
are highly configurable, such as the adaptive logic module (ALM) for Stratix® series
or logic element (LE) for Cyclone® series FPGAs. It is impractical to generate
measurement patterns that cover all supported configurations of these blocks.
Instead, the block is broken up into sub-blocks, each with a small number of
supported configurations. The detailed block power model is developed by
simulating every configuration of each sub-block and modeling the resource as a
network of these sub-blocks. The simulation-based power models are then
incorporated into the PowerPlay power analysis tools and correlated to silicon
measurements.
While other unsophisticated power analysis tools treat each block as a simple black
box where only input and output transitions are measured, PowerPlay power analysis
also measures each block’s internal transitions. For example, if the input of a simple
register is toggled and the clock held high, as shown in Figure 3, a black box model
would simply assume that because the output does not toggle, no power is consumed.
Figure 3. Simple Register
The PowerPlay power analyzer, however, can still accurately predict power
consumed in internal nodes by modeling the register’s entire internal structure, as
shown in Figure 4.
December 2010
Altera Corporation
FPGA Power Management and Modeling Techniques
Page 4
Power Models
Figure 4. Internal Toggling of Register
Another example is a simple 2-input AND function that can be implemented in an
ALM or LE. If one input is held low and another input toggles, a black box model
would assume that no power is consumed because the output does not toggle.
However, PowerPlay power analysis tools consider every internal multiplexer, buffer,
and wire in the block, so power consumed by internal toggles is correctly counted, in
this case by the first-stage multiplexer of the look-up table (LUT) shown in Figure 5.
Figure 5. Internal Toggling of AND Gate
Routing Power Model
A significant portion of total dynamic power is consumed in the programmable
routing fabric of the FPGA. Dynamic routing power has two main components: shortcircuit current and power dissipated in charging and discharging load capacitances.
Figure 6 illustrates how power is consumed when an inverter switches a capacitive
load.
Figure 6. Power Consumed in an Inverter
Energy E = CV2
drawn from supply
No energy drawn
from supply
Load
capacitance
discharged
2
½ CV dissipated
in pull-up
Falling
transition at
buffer input
½ CV 2
stored on
capacitor
Rising
transition at
buffer input
½ CV 2
dissipated
in pull-down
For FPGA routing switches, the load is the lumped capacitance of all downstream
circuitry, including the metal capacitance of the wire and other interconnect, and the
input capacitances of multiplexers and gates that listen to the wire. The loading on a
typical FPGA routing wire is shown in Figure 7.
FPGA Power Management and Modeling Techniques
December 2010 Altera Corporation
Power Models
Page 5
Figure 7. Loading on a Typical FPGA Wire
CIN “OFF”
CIN “ON”
CWIRE
Interconnect
Capacitance
The input capacitance of each multiplexer is determined by its configuration.
Quartus II software uses a transistor-level model of each listening multiplexer to
accurately calculate its input capacitance based on configuration and type.
The metal capacitance of the wire is determined by its length, thickness, separation
from its neighbors, and the metal layer where it is implemented. Quartus II software
has a database with the precise capacitance of every routing wire on the device for use
in power and timing analysis. These capacitances are extracted from device layouts
and account for all physical and geometric effects.
For most circuits, charging load capacitances is the dominant component of dynamic
power. However, the FPGA routing fabric uses large inverters to drive long
interconnect lines. These drivers can conduct significant short-circuit currents, so this
effect cannot be ignored.
Short-circuit current occurs because switching signals cannot transition
instantaneously-they have non-zero rise and fall times. During the transition, the pullup and pull-down transistors of the inverter are both turned on for a period of time.
Current flows directly from the supply to ground and is dissipated in the NMOS and
PMOS transistors. Figure 8 shows the state of a switching inverter when its input is
crossing through the half-supply point.
Figure 8. Short Circuit Current
VIN = VDD/ 2
VSG = VDD/ 2
ISC
VGS = VDD/2
December 2010
Altera Corporation
FPGA Power Management and Modeling Techniques
Page 6
Power Models
For an inverter, power consumption due to short-circuit current scales roughly
linearly with input transition time. Slower input transitions create a larger shortcircuit window where both transistors are turned on, so more power is consumed. In
an FPGA’s programmable routing fabric, the transition time at the input to each buffer
depends strongly on device configuration and physical wire parameters. Interconnect
wires have significant distributed resistance and capacitance. As a transition
propagates along a net, its edge rate is degraded by resistor capacitor (RC) filtering.
Multiplexers listening to the wire add load capacitance along its length and worsen
this effect, as shown in Figure 9.
Figure 9. Effect of Load Capacitance on Edge Rate
Input edge
rate from
previous
stage
Fast edge
near the driver
Slow edge at the
end of the wire
Determining accurate transition times at buffer inputs requires a detailed analysis of
each wire. Physical wire parameters and loading effects (including the type and
position of each load) must be considered.
During compilation, Quartus II software performs a transistor-level simulation of
every routing path in the design using a fast internal SPICE-like simulator. The
extracted capacitance and resistance of each wire are combined with information
about the input capacitance and position of each listening multiplexer to determine
the edge rate at each position along the net. Precise rising and falling transition times
are calculated at the input to each routing switch. These waveforms are used as the
input to subsequent simulations and accurate edge rates are propagated through the
circuit. The total capacitive load seen by each switch is calculated based on the
configuration of its fan-out. These values are used to accurately predict the shortcircuit and capacitive switching power consumed in each routing element.
Accurate modeling of dynamic routing power is a critical step in the power analysis
flow. The Quartus II PowerPlay power analyzer combines detailed device information
and physical models with a sophisticated transistor-level simulation engine. The
result is an accurate estimate of dynamic routing power that accounts for the
numerous physical phenomena affecting the operation of deep-sub-micron VLSI
circuits.
I/O Power Models
The I/O power model is another simulation-based power model with a very unique
feature. With Altera’s device families, such as Stratix III and Cyclone III FPGAs, the
PowerPlay power analyzer does not simply model each I/O pin as having a
capacitive load. Instead, it takes into account every possible parameter describing the
off-chip board trace at each I/O pin, including relevant termination networks and
transmission line effects, as shown in Figure 10.
FPGA Power Management and Modeling Techniques
December 2010 Altera Corporation
Accuracy
Page 7
Figure 10. Advanced I/O Power Board Trace Model
On Chip
Vt
Off Chip
Vtt
Rnh
T_length
Rns
Package Model
(Determined by
Quartus II Software)
Cn
L_per_length
Rnl
C per length
Rfh
Rfs
Cf
Rfl
When the PowerPlay power analyzer performs advanced I/O power analysis, the
entire board trace model is simulated using a sophisticated SPICE-like simulator built
into the Quartus II design software. These simulations run for each I/O, allowing the
PowerPlay power analyzer to calculate an extremely accurate power estimate specific
to each individual customer’s board design.
Empirical Dynamic Power Models
Empirical dynamic power models are based entirely on measured data. These models
are used for blocks, such as embedded SRAM memories and embedded multipliers,
that are too large to simulate in a reasonable amount of time, but have a small enough
set of supported configurations that a parameterized measured model will suffice.
The development methodology is very straightforward and very accurate. The best
way to accurately measure the power of a single block in a specific configuration in
the FPGA is to configure the FPGA with all instances of a block measured in the
configuration state under analysis. All other logic and functional blocks are
configured for the lower power operating mode and are not stimulated. Then, welldesigned and repeatable stimulus patterns are run through all instances of the block
being measured to generate an understood power profile. The resulting power
consumed by the chip is largely the result of the large number of blocks under test,
and the excess power can be subtracted from the total power. The resulting power,
divided by the number of blocks configured, gives an accurate view of power for that
mode of that block.
Accuracy
Altera’s PowerPlay early power estimator has the industry’s most accurate models of
the functional components within the FPGA. Because it is used before an RTL design
is available, however, it lacks critical information such as logic configuration,
placement, and routing, limiting its overall accuracy. The Quartus II PowerPlay
power analyzer is a far more detailed power analysis tool that uses actual design
placement and routing and logic configuration, and can use simulated waveforms to
estimate dynamic power very accurately. On average, the PowerPlay power analyzer
usually provides ±10% accuracy when used with accurate design information.
PowerPlay power analysis tools power models closely correlate to actual silicon
measurements. Altera uses over 8,500 different test configurations to measure the
power of individual blocks on a device (see Figure 11). Each configuration measures a
single FPGA circuit component in a specific configuration.
December 2010
Altera Corporation
FPGA Power Management and Modeling Techniques
Page 8
Accuracy
Figure 11. PowerPlay Power Analyzer vs. Silicon Measurements for All RAM Configurations
In addition, Altera has built up an extensive suite of customer designs that are
frequently used to test the overall accuracy of the PowerPlay power analyzer. These
designs are compiled and simulated, and then the power is analyzed using the
PowerPlay power analyzer. These power predictions are compared to actual silicon
measurements (Figure 12), ensuring that the PowerPlay power analysis tools are not
only accurate when analyzing specific blocks, but that they are also accurate when
predicting power for designs that reflect what real customers are compiling.
Figure 12. PowerPlay Power Analyzer vs. Silicon Measurements for Customer Designs
1400
Core Dynamic Power (mW)
1200
1000
800
Silicon
Quartus II
600
400
200
0
Designs
FPGA Power Management and Modeling Techniques
December 2010 Altera Corporation
Conclusion
Page 9
Conclusion
Altera’s PowerPlay early power estimator and the Quartus II PowerPlay power
analyzer provide best-in-class tools to deliver accurate estimation of power
consumption from early design concept through design implementation. These tools
not only help designers verify that the design is within the power and thermal
management budget, but also optimize designs for power. As process shrinks and
increasing design complexity make power a more important issue for designers, the
PowerPlay power analysis tools provide the most advanced power analysis tools for
Altera FPGAs today.
Acknowledgements
■
Bryce Leung, Advanced Software Engineer, Software Engineering, Altera
Corporation
■
Jeffrey Chromczak, Advanced Software Engineer, Software Engineering, Altera
Corporation
■
Jennifer Farrugia, Software Engineering Supervisor, Software Engineering, Altera
Corporation
Document Revision History
Table 1 shows the revision history for this document.
Table 1. Document Revision History
Date
Version
December 2010
November 2007
December 2010
2.0
1.0
Altera Corporation
Changes
■
Added Abstract.
■
Minor text edits.
■
Updated Figure 2 and Figure 6.
Initial release.
FPGA Power Management and Modeling Techniques
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