Long Presentation: Topic 8
General Low Power/Energy
Optimization Techniques
Jordan Templeton
EEL6935G
On the Interplay of Dynamic Voltage Scaling and Dynamic
Power Management in Real-Time Embedded Applications
Vinay Devadas, Dept. of Computer Science, George Mason University
Fairfax, VA, USA
Hakan Aydin, Dept. of Computer Science, George Mason University
Fairfax, VA, USA
Introduction
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Major reason for increased energy
efficiency is to promote longer run time of
portable, battery powered embedded
devices.
Two main paths for increasing efficiency
are typically employed exclusively.
Dynamic Voltage Scaling (DVS)
Dynamic Power Management (DPM)
Objectives
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Maximize run time of a real-time system
that operates from a finite source of
energy.
Maintain real-time system timing accuracy.
Dynamic Voltage Scaling
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Main goal is to reduce energy
consumption of CPU.
CPU energy consumption increases with
system operating speed.
Energy management can be realized
through management of CPU clock speed.
Real-time systems are susceptible to
failure from excessively slow clock speeds.
Pros/Cons of DVS
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Care must be taken to avoid slowing the
system down too much and causing
missed deadlines.
DVS is widely used and well understood
making implementation relatively
straightforward.
Dynamic Power Management
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Energy savings are realized through selective
sleep periods for system peripherals such as
memory and I/O devices.
Effective implementation requires accurate
prediction of upcoming allowable sleep
periods.
Timing constraints are critical for real-time
systems because of peripheral wake-up
delay.
Pros/Cons of DPM
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Requires accurate prediction of duration of
sleep state to determine if a transition is
worth it, called the break-even time.
Highly beneficial for energy savings for
devices that have low duty cycles.
DVS/DPM Interaction
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Most research is focused on employing
one or the other; little is known about
combining them.
DVS reduces CPU speed which leads to
longer execution times and less
opportunity for devices to transition to
sleep states.
Increased CPU speeds allows for longer
sleep periods which is a benefit of DPM.
Design Work
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Define system model and assumptions
Analyze the interaction between DVS and
DPM for a simple single device model.
Expand analysis to a general, multiple
device model.
Device Parameters
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Pa: Active state power consumption.
Ps: Sleep state power consumption.
Tsd: Time to transition to sleep mode.
Twu: Time to transition to active mode.
Esd: Energy overhead for transition to sleep mode.
Ewu: Energy overhead for transition to active mode.
Single Device Model
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Simplified version of proposed algorithm
to illustrate concept.
Applicable only to simplest
implementations; not widely useful.
Single Device Break-Even Time
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Minimum allowable sleep time for a device to
be able to make use of a sleep mode transition.
c = WCET
d = frame length
B0 = break-even time
Energy Consumption Tradeoffs
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Region 1 illustrates operation where CPU speed is too slow to allow
enough time for devices to enter sleep mode.
Region 2 illustration where CPU speed has increased enough to
allow sleep modes to be utilized.
f* is the minimum frequency at which the CPU operates fast enough
to allow sleep modes.
Figure 2 (b) shows that it may
still be possible to achieve the
lowest energy consumption
while only using DVS and a very
low CPU frequency.
Multiple Device Model
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Generalized algorithm to service a broader
range of applications.
Includes support for devices with differing
parameters (Pa, Ps, Esd, Ewu, Tsd, Twu).
Each device has its own break-even time.
Multiple Device Break-Even Time
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m = number of devices
c = WCET
d = frame length
Bm = break-even time of device m.
Break-even times arranged in ascending order of length from right,
i.e. Bm > Bm-1 > B2 > B1.
Conclusion
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DVS and DPM are much more difficult to implement
when combined compared to individually.
The combination of DVS and DPM is necessary for
system level designers to progress in the quest for
maximum embedded energy efficiency.
The authors acknowledge that their work is some
of the first in this area of research and much more
refinement is necessary; they view their work more
as a proof of concept rather than a finished
product.
Power Management in Real Time Embedded Systems through Online
and Adaptive Interplay of DPM and DVFS Policies
Khurram Bhatti, LEAT Research Laboratory, University of Nice-Sophia Antipolis
Valbonne, France
Cecile Belleudy, LEAT Research Laboratory, University of Nice-Sophia Antipolis
Valbonne, France
Michel Auguin, LEAT Research Laboratory, University of Nice-Sophia Antipolis
Valbonne, France
Introduction
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Much research is focused on increasing energy
efficiency of embedded real-time systems.
Today’s devices are becoming smaller and
smaller, resulting in less battery capacity and
cooling capabilities.
At the same time, processing power is
increasing.
These are the two major factors fueling the
need to increase efficiency.
Previous Work
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Dynamic Power Management (DPM) is a well established
method of reducing energy consumption via selectively
enabling sleep mode for various system devices.
Dynamic Voltage and Frequency Scaling (DVFS) is
another heavily used technique to regulate energy
consumption via CPU performance control.
Both of these techniques are very well understood when
applied individually but little effort has been put toward
utilizing them together.
Early work in the area of combining DPM and DVFS has
been to use DPM only for system devices and DVFS only
for the CPU.
Hybrid Power Management
Design Proposal
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Incorporate and apply existing energy management
policies relating to both DPM and DVFS.
Primarily a control system rather than a unique power
management technique.
Proposed adaptive scheme to determine best policy for
the current conditions at runtime is called HyPowMan.
HyPowMan is designed for multiprocessor real-time
systems but can also be used on single processor
systems.
Control over each processor can be global or partitioned.
System Model and Notations
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m processors : P = {P1, P2, …, Pm}
All processors support both DPM and DVFS with
independent control for each processor.
DVFS is assumed to scale over a continuous range but
the model also supports discrete ranges.
Voltage and frequency are always adjusted together.
There is to be a finite number of tasks executed over a
frame of time, TS = {Ta1, Ta2, …, Tan}.
Each task Tan within TS is governed by at least four
characteristics (ri, Ci, di, Ti) referring to release time,
worst-case execution time, relative deadline, and
periodicity.
HyPowMan Scheme
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Created as a policy selection mechanism.
A library contains all energy management schemes and a
machine-learning algorithm is used to select a set of
participating policies, known as an expert set.
Each policy that is participating is called an expert.
Inactive policies are referred to as dormant policies.
All DPM based experts become dormant when a CPU is
executing a task.
DVFS based experts are dormant during CPU idle periods.
Active experts are termed working experts.
Fundamental Design Challenge
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HyPowMan must be able to decide which expert
to utilize but all of the required information for
such a decision is not available at once.
Experts employing DPM rely on schedule
information to adjust to idle time intervals.
DVFS experts adjust according to dynamic slack
that varies depending on workload.
HyPowMan seeks the best energy policy within a
given expert set, not the absolute best possible
energy policy.
How It Works
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Machine Learning Algorithm
Assign weight vector Winput and probability
vector Hinput to the expert set.
N: number of experts
1≤k≤N
0 ≤ hkinput ≤ 1
Expert with highest probability is chosen.
Evaluating Expert Performance
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Experts are evaluated at completion of
current task.
Working expert evaluation based on
amount of energy saved.
Dormant expert evaluation based on
potential energy conservation
performance using loss factor lkinput for
expert k.
Loss Factor Computation
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, (0 ≤ α ≤ 1)
Loss factor Lkinput related to energy savings
and performance degradation.
Lkinput is used to update weight factors:
where 0 ≤ β ≤ 1 .
β is dependent on granularity of weight
factors and is higher for lower variations in
weight factors for a given input.
Summary of Selection Algorithm
Experimental Evaluation
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Simulated using STORM (Simulation Tool for Real-time
Multiprocessor Scheduling).
Marvell Xscale based PXA270 processor.
Measurements of energy consumption for three different
parameters
Energy Consumption vs.
BCET/WCET Ratio
Energy Consumption vs.
Number of Tasks
Energy Consumption vs. Variation
in Cumulative Utilization
Energy Consumption vs. Alpha
Energy Consumption vs. Beta
Conclusion
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HyPowMan chooses from a library of existing
energy management policies rather than create a
new policy.
The selection algorithm is adaptive and can
compensate for varying conditions.
The scheme allows for multiple types of energy
policies to be employed by a single system.
The system overhead of HyPowMan increases as
the number of experts grows but that also
improves the energy conservation performance.
Comparisons
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Paper 1 proposes a new energy
management policy that is a combination
of DPM and DVFS while paper 2 proposes
a selection at runtime of the best existing
energy management policy from a library.
Paper 1 is primarily aimed at single
processor systems while paper 2 mainly
discusses multi-processor systems.
Critiques
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Paper 1 does not provide enough
experimental data or reasonable
supporting justification of its claims.
Paper 2 claims that the protocol works on
single processor systems as well, but
there are no simulation results to compare
the performance to the multi-processor
case.
References
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Paper 1
Devadas, Vinay; Aydin, Hakan; , "On the Interplay of Voltage/Frequency Scaling and Device
Power Management for Frame-Based Real-Time Embedded Applications," Computers, IEEE
Transactions on , vol.61, no.1, pp.31-44, Jan. 2012
Paper 2
Bhatti, K.; Belleudy, C.; Auguin, M.; , "Power Management in Real Time Embedded Systems
through Online and Adaptive Interplay of DPM and DVFS Policies," Embedded and Ubiquitous
Computing (EUC), 2010 IEEE/IFIP 8th International Conference on , vol., no., pp.184-191, 1113 Dec. 2010