On-line thermal aware dynamic voltage scaling for energy optimization with frequency/temperature dependency consideration.
- SourceAvailable from: Zebo Peng[show abstract] [hide abstract]
ABSTRACT: Dynamic voltage selection and adaptive body biasing have been shown to reduce dynamic and leakage power consumption effectively. In this paper, we optimally solve the combined supply voltage and body bias selection problem for multiprocessor systems with imposed time constraints, explicitly taking into account the transition overheads implied by changing voltage levels. Both energy and time overheads are considered. The voltage selection technique achieves energy efficiency by simultaneously scaling the supply and body bias voltages in the case of processors and buses with repeaters, while energy efficiency on fat wires is achieved through dynamic voltage swing scaling. We investigate the continuous voltage selection as well as its discrete counterpart, and we prove strong NP-hardness in the discrete case. Furthermore, the continuous voltage selection problem is solved using nonlinear programming with polynomial time complexity, while for the discrete problem, we use mixed integer linear programming and a polynomial time heuristic. We propose an approach that combines voltage selection and processor shutdown in order to optimize the total energyIEEE Transactions on Very Large Scale Integration (VLSI) Systems 04/2007; · 1.22 Impact Factor
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ABSTRACT: Supply voltage scaling and adaptive body biasing (ABB) are important techniques that help to reduce the energy dissipation of embedded systems. This is achieved by dynamically adjusting the voltage and performance settings according to the application needs. In order to take full advantage of slack that arises from variations in the execution time, it is important to recalculate the voltage (performance) settings during runtime, i.e., online. However, optimal voltage scaling algorithms are computationally expensive, and thus, if used online, significantly hamper the possible energy savings. To overcome the online complexity, we propose a quasi-static voltage scaling (QSVS) scheme, with a constant online time complexity O(1). This allows to increase the exploitable slack as well as to avoid the energy dissipated due to online recalculation of the voltage settings.IEEE Transactions on Very Large Scale Integration (VLSI) Systems 02/2011; · 1.22 Impact Factor
Conference Proceeding: Dynamic and aggressive scheduling techniques for power-aware real-time systems[show abstract] [hide abstract]
ABSTRACT: In this paper we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that the reclaiming algorithm saves a striking 50% of the energy, over the static algorithm. Further our speculative techniques allow for an additional approximately 20% savings over the reclaiming algorithm. In this study, we also establish that solving an instance of the static power-aware scheduling problem is equivalent to solving an instance of the reward-based scheduling problem [1, 4] with concave reward functions.Real-Time Systems Symposium, 2001. (RTSS 2001). Proceedings. 22nd IEEE; 01/2002
On-line Thermal Aware Dynamic Voltage Scaling for
Energy Optimization with Frequency/Temperature
With new technologies, temperature has become a major issue to
be considered at system level design. Without taking temperature
aspects into consideration, no approach to energy or/and perfor-
mance optimization will be sufficiently accurate and efficient. In
this paper we propose an on-line temperature aware dynamic volt-
age and frequency scaling (DVFS) technique which is able to ex-
ploit both static and dynamic slack. The approach implies an off-
line temperature aware optimization step and on-line voltage/freq-
uency settings based on temperature sensor readings. Most impor-
tantly, the presented approach is aware of the frequency/temperature
dependency, by which important additional energy savings are ob-
Categories and Subject Descriptors
C.3 [Special-Purpose and Application-Based Systems]: Mi-
croprocessor/microcomputer applications, real-time and embedded
systems; D.4.1 [Operating Systems]: Process Management—
scheduling; J.6 [Computer-Aided Engineering]: computer-aided
design; J.7 [Computers in Other Systems]: real time
Algorithms, Design, Performance, Theory
temperature dependency, energy, voltage/frequency scaling
Technology scaling and ever increasing demand for performance
have resulted in high power densities in current circuits, which
not only results in huge energy consumption but also leads to in-
creased chip temperature. Temperature has become a major issue
to be considered at system level design. Of particular importance
in this context is the development of adequate temperature mod-
eling, analysis, and measuring tools. HotSpot  is an archi-
tecture and system-level temperature model and simulator, based
on elaborating an equivalent circuit of thermal resistances and ca-
pacitances that correspond to the architecture blocks and to the
elements of the thermal package. A similar approach is proposed
in  where dynamic adaptation of the resolution is performed,
in order to speed up the thermal analysis.
temperature models, which are much less accurate, have been also
proposed . For on-line temperature monitoring, sensors have
been used  together with techniques for collecting and analyz-
ing their values with adequate accuracy .
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DAC 2009, July 26 - 31, 2009, San Francisco, California, USA.
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Several approaches to thermal aware system-level design aiming
at energy optimization or temperature control have been proposed.
Static approaches are exclusively based on temperature models
used at design time. For example, in , a simulated-annealing
based thermal aware floorplanning approach has been proposed.
Thermal aware task allocation and scheduling have been addressed
in  while in  an approach to task scheduling under peak
temperature constraints is presented. An approach to design space
exploration for multiprocessor SoCs under area and thermal con-
straints is presented in .
Various techniques have been proposed in which decisions are
taken based on temperature measurements on the chip at execu-
tion time. Jung proposed a temperature management ap-
proach based on a Markovian decision process aiming at minimiz-
ing energy under temperature constraints. The abstract model
of the managed system is constructed at design time, based on
a very simple temperature model. Using this off-line generated
abstract model, at run time, decisions are taken based on temper-
ature sensor reading. No strict timing constraints are considered.
The techniques proposed in for dynamic OS-level workload
scheduling are exclusively based on execution time temperature
sensor reading. The goal is to avoid thermal hot spots and large
temperature variations which have a negative impact on system
One of the preferred approaches for reducing the overall energy
consumption is dynamic voltage/frequency scaling (DVFS). This
technique exploits the available slack times by reducing the voltage
and frequency at which the processors operate and, thus, achieves
energy efficiency. There are two types of slacks: (1) static slack,
which is due to the fact that, when executing at the highest (nom-
inal) voltage level, tasks finish before their deadline even when
executing their worst number of cycles (WNC); (2) dynamic slack,
due to the fact that most of the time tasks execute less cycles than
their WNC. Offline DVFS techniques ,  can only exploit
static slack while online approaches , ,  are able to fur-
ther reduce energy consumption by exploiting the variation of the
workload generated by the tasks. None of the above approaches
considers any implication of the chip temperature.
As mentioned earlier, high power densities on current micro-
processors result in high energy consumption and increased chip
temperature. Growing temperature leads to an increase in leakage
power and, consequently, energy, which, again, produces higher
temperature. Thus, temperature is an important parameter to be
taken into consideration at voltage selection. However, very few
of the proposed DVFS techniques are considering the tempera-
ture issue. In  an offline, static DVFS scheme is presented,
aimed at reducing peak temperature. In
a temperature and leakage aware offline DVFS approach for en-
ergy minimization. The approach proposed in  is based on a
design time optimization procedure which is performed consider-
ing various start time temperatures and workloads. At run-time,
frequency setting is based on actual temperatures received from
sensors. The approach ignores the dependency of leakage (and,
consequently, energy) on temperature and assumes (as in offline
DVFS techniques) that the number of cycles executed by a given
task is fixed and known at design time.
The basic idea of DVFS approaches is to achieve energy mini-
mization by reducing the supply voltage. Since frequency depends
on the voltage, every change in supply voltage has to be followed,
in principle, by a frequency adjustment. In order to provide per-
formance, the frequency is usually set to the maximum value al-
lowed by the current supply voltage. However, temperature has
an important impact on circuit delay and, implicitly, on frequency,
 we have proposed
mainly through its influence on carrier mobility and threshold volt-
age . Thus, the frequency does not only depend on the voltage
but also on the temperature. This aspect has been completely
ignored even by temperature aware approaches to DVFS. In fact,
when calculating the allowed frequency for a given supply voltage
Vdd, it is implicitly assumed that this is the frequency f corre-
sponding to a maximum temperature Tmax at which the chip is
allowed to run. While this is a safe assumption, it is far from
efficient. If we are aware that the chip is running at a tempera-
ture T < Tmax, the frequency could be fixed at f?> f and, thus,
performance is increased for the same energy consumption. Or,
maybe more important, the same frequency f could be achieved
with a supply voltage V?
As we will show in this paper, such a temperature aware dynamic
frequency setting can be an important contributor to energy effi-
The main contribution of this paper is an online temperature
aware DVFS approach which can exploit both static and dynamic
slack.The approach implies an offline temperature aware op-
timization step and online voltage/frequency settings based on
temperature sensor reading. Most importantly, the presented ap-
proach is aware of the frequency/temperature dependency by which
important additional energy savings are obtained.
The paper is organized as follows: In section 2 we introduce the
power, delay and application model as well as the voltage selec-
tion techniques used in the paper. Section 3 gives a motivational
example. In section 4 we present our DVFS approaches. Finally,
experimental results and conclusions are presented in sections 5
and 6 respectively.
dd< Vddand, thus, further energy is saved.
Power and Delay Model
For dynamic power we use the following equation , :
Pdyn= Ceff∗ f ∗ V2
where Ceff, Vdd, and f denote the effective switched capacitance,
supply voltage, and frequency, respectively.
The leakage power is expressed as follows , , :
Pleak= Isr∗ T2∗ e
where Isr is the reference leakage current at reference temperature.
T is the current temperature, Vbs is the body bias voltage, and
Iju is the junction leakage current. α, β and γ are curve fitting
circuit technology dependent coefficients.
The frequency at a reference temperature is calculated as follows
K6∗ Ld ∗ Vdd
where Ld is the logic depth. K1, K2, K6, and vth1 are technology
dependent coefficients. α reflects the velocity saturation imposed
by the used technology (common values 1.4 < α < 2). The scaling
of frequency with temperature is given by equ.4  :
∗ Vdd+ |Vbs| ∗ Iju
d=((1 + k1) ∗ Vdd+ K2∗ Vbs− vth1)α
f ∝(Vdd− (vth1+ k ∗ (T − Tref)))ξ
T is the temperature while k, ξ, and μ are empirical technology
2.2Application and System Model
The functionality of the application is captured as a set of task
graphs. In a task graph G(Π,Γ), nodes τ ∈ Π represent computa-
tional tasks, while edges η ∈ Γ indicate data dependencies between
tasks (communication). Each task is characterized by the follow-
ing parameters: the worse case (WNC), best case (BNC), and
expected (ENC) number of clock cycles to be executed, a dead-
line, and the average switched capacitance. ENC is the arithmetic
mean value of the probability density function p(NC) of the task
execution cycles NC, i.e., ENC =?WNC
tem with a voltage scalable processor which can operate at several
discrete supply voltage levels. The processor has internal temper-
ature sensors that can be accessed during execution. The appli-
cation and a set of look up tables (LUT), one for each task, are
stored in memory. The LUTs are generated offline and are used
during execution whenever the scheduler has to adjust the proces-
sor’s performance to the appropriate level, via voltage/frequency
scaling. An appropriate performance level allows the tasks to meet
their deadlines while maximizing the energy savings.
j=BNCj ∗ pj(j)
The application is mapped and scheduled on an embedded sys-
2.3 Temperature-Aware DVFS
 we have presented a DVFS approach which given a
mapped and scheduled application, calculates the appropriate volt-
age levels for each task, such that the total energy consumption
is minimized.Another input to the algorithm is the dynamic
power profile of the application, which is captured by the aver-
age switched capacitance of each task. This information will be
used for calculating the dynamic energy consumed by the task ex-
ecuted at certain supply voltage levels according to equ.1. The
leakage energy is calculated using equ.2. However, since leakage
strongly depends on temperature, an obvious question is which
temperature to use for leakage calculations. Ideally, it should be
the temperature at which the chip will work when executing the
application. This temperature, however, is not known, since the al-
gorithm is just calculating the voltages at which to run the system
and these voltages are influencing the energy dissipation which,
again, is determining the temperature.
The algorithm in  requires the designer to introduce an as-
sumed temperature which is used at energy optimization. This, of
course, leads to suboptimal results, since the temperature used for
energy calculation during voltage selection is different from the
actual temperature at which the chip works. In order to over-
come the above problem, in  we have proposed a temperature
aware DVFS technique which is based on an iterative approach
as illustrated in Fig.1. The approach starts from an initial ”as-
sumed” temperature, at which the processor is supposed to run.
The voltage selection algorithm will determine, for each task, the
voltage levels such that energy consumption is minimized. Based
on the determined voltage (and the switched capacitances known
for each task) the dynamic power profiles are calculated, the ther-
mal analysis is performed, and the processor temperature profile
is determined in steady state. This new temperature information
is now used again for voltage selection and the process is repeated
until the temperature converges. Convergence means that the ac-
tual temperature values used at voltage selection correspond to
the temperature at which the chip will function when running
with the calculated voltages. As shown in , in most of the cases,
convergence is reached in less than 5 iterations.
Figure 1: Temperature-aware Voltage Selection
Thermal analysis in our DVFS techniqueis based on the HotSpot
environment . Our modifications to HotSpot, in order to cap-
ture the dependence of leakage on temperature, are presented in
Two limitations of the above approach are important in the
context of this paper:
1. It ignores frequency/temperature dependency and, thus, pro-
duces solutions which are excessively conservative.
2. It is a static technique, assuming that tasks always execute
their WNC and, thus, cannot exploit dynamic slack.
In this section, we consider an application consisting of three
tasks as shown Fig.2. The WNC of τ1, τ2, and τ3 is 2.85 ∗ 106,
1.0 ∗ 106, and 4.30 ∗ 106, respectively, and their average switched
capacitance (in F) is 1.0 ∗ 10−9, 0.9 ∗ 10−10, and 1.5 ∗ 10−8, re-
spectively. The application has a global deadline of 0.0128s. We
assume that the three tasks are executed on a processor which has
9 discrete Vdd levels from 1.0V to 1.8V with a step of 0.1V. The
chip size is 0.007m*0.007m with a peak working temperature of
Tmax = 125◦C.
For the above example we perform energy minimization using
the temperature-aware DVFS method outlined in section 2.3. As
mentioned, this DVFS approach (like all other in literature) ig-
nores the frequency/temperature dependency and, when calculat-
ing the maximum allowed frequency for a certain supply voltage,
Figure 2: Motivational Example
the maximum allowed working temperature for the chip Tmax=
125◦C is considered. In Table 1 we show the actual voltages and
frequencies for each task, as calculated by the DVFS algorithm,
and the consumed energy. We also show the peak temperature
for each task, obtained with dynamic thermal analysis. As can be
observed, this peak temperature is far below the Tmax of the chip.
Table 1: DVFS without Frequency/Temperature Depen-
Task Peak Temp(◦C) Voltage(V)
From equ. 3 and 4 it is obvious that by taking into consid-
eration the actual temperature at frequency calculation there is
a large margin for reducing the supply voltage without compro-
mising on performance. We have performed a DVFS based energy
optimization, similar to the one above, but with the difference that
frequencies corresponding to the different voltage settings are cal-
culated taking into consideration the peak temperature at which
the actual task runs. Table 2 shows the results. We can see that
a substantial energy reduction of 33% has been obtained.
DVFS with Frequency/Temperature Depen-
The DVFS approach used above is an off-line, static one which
assumes that tasks execute their WNC and, thus, can only exploit
the static slack. However, in reality, there are huge variations in
the number of cycles executed by a task, from one activation to
the other, which leads to an important amount of dynamic slack.
Imagine an activation scenario for which each of the three tasks in
Fig. 2 execute a number of cycles equal to 60% of their WNC. If
we use the offline DVFS approach above and run at the voltages
calculated as in Table 2 the total energy consumption would be
0.122J. However, much more can be done by also exploiting the
dynamic slack. This implies that, at run-time, whenever a task
terminates, the voltage level and the frequency for the next task
is calculated by taking into consideration the current time and the
current chip temperature. Table 3 shows the voltage and frequency
levels determined in this way as well as the corresponding energy
consumption. The total energy consumed is 0.106J, which means
a reduction of 13.1% compared to the off-line DVFS approach.
The examples presented in this section demonstrate that (1)
considering the frequency/temperature dependency at DVFS can
lead to substantial energy savings and (2) an on-line temperature
aware approach is needed in order to make use of the dynamic
slack created due to variable number of clock cycles executed at
4.DVFS WITH FREQUENCY/TEMPERATURE
The static approach is based on the technique outlined in section
2.3 and described in detail in. As can be observed in Fig.
1, the successive iterations are leading, after convergence, to a
temperature profile which corresponds to the one at which the chip
will work. This temperature profile is used for energy calculation
by the voltage/frequency selection algorithm.
For each task τithe voltage/frequency selection algorithm calcu-
lates a certain supply voltage Vddisuch that energy consumption
is minimized and deadlines are satisfied. When calculating the
frequency setting for τi, as opposed to  and all other previous
approaches, we now consider the thermal profile of the task and
determine the maximum temperature Tpeakiat which that task
runs. At voltage/frequency selection, the frequency is calculated
Table 3: Dynamic DVFS
Peak Temp(◦C) Voltage(V)
based on equ. 3 and 4 (instead of being fixed, in a conservative
way, considering the worst case temperature Tmax for which the
chip is designed).
4.2 Dynamic Approach
The above approach determines start times for tasks and their
voltage/frequency levels assuming that they execute their WNC.
By this, only static slack is considered for energy minimization1.
In order to exploit the dynamic slack, at the termination of each
task and before starting the next one, voltage and frequency set-
tings have to be determined based on the values of the current time
and temperature. In principle, calculating the appropriate volt-
age/frequency settings implies the execution of the temperature
aware DVFS algorithm from section 4.1. Running this algorithm
on-line, after each task execution, implies a huge time and energy
overhead which can be even higher than the execution time and
energy consumption of the actual application.
To overcome the above problem, we have divided our dynamic
DVFS approach into two phases. In the first phase, performed
off-line, voltage/frequency settings for all tasks are pre-computed,
based on possible start times of the tasks and the possible temper-
atures at that start time. The resulting voltage/frequency settings
are stored in look-up tables (LUTs), one for each task. In Fig. 3 we
show two such tables. They contain voltage and frequency settings
for combinations of possible start time ts and start temperature
Ts of a task. For example, the line with start time 1.3 and start
temperature 55 stores the voltage and frequency setting for the
situation when τ2 starts in the time interval (1.2s,1.3s] and the
start temperature is in the interval (45◦C,55◦C]. In section 4.2.1
we will present the generation procedure of the LUTs.
The second phase is performed on-line and is illustrated in Fig.
3. Each time a task terminates and a new voltage/frequency level
has to be fixed for the next one, the on-line scheme looks up the ap-
propriate setting from the LUT, depending on the actual time and
temperature reading. If there is no exact entry in the LUT corre-
sponding to the actual time/temperature, the entry corresponding
to the immediately higher time/temperature is selected. For ex-
ample, in Fig.3, τ1 finishes at time 1.25s with a temperature 49◦C.
To determine the appropriate voltage and frequency for τ2, LUT2
is accessed based on these time and temperature values. There is
no exact entry for 1.25s and 49◦C, so the entry corresponding to
start time 1.3s and start temperature 55◦C is chosen. This on-line
phase indicated with VS at Fig. 3 is of very low, constant time
complexity O(1) and, thus, very efficient.
Figure 3: On-line Phase
Considering an application and a system as described in section
2.2, the goal is to generate a LUT for each task τi, such that the
1It should be mentioned that, as opposed to the dynamic one, the
static approach can be used even in the case that no temperature
sensors are available on the chip.
energy consumption during execution is minimized. The task exe-
cution order is fixed according to a scheduling policy (e.g. EDF).
According to this order, task τi has to be executed after τi−1 and
before τi+1. It is important to notice that the voltage levels and
frequencies are calculated so that the energy consumption is opti-
mal in the case that the tasks execute their expected number of
cycles ENC (which, in reality, happens with a much higher proba-
bility than e.g. the WNC). Nevertheless, voltages and frequencies
are fixed such that, even in the worst case (tasks execute WNC),
deadlines are satisfied.
The LUT generation algorithm is presented in Fig.4. The outer-
most loop iterates over the set of tasks and successively constructs
the table LUTi for each task τi. The next loop generates the en-
tries of LUTi corresponding to the various start temperatures Tsi
of τi. Finally, the innermost loop iterates, for each possible start
temperature, over all considered start times tsiof task τi. The al-
gorithm starts by computing the earliest and latest possible start
times for each task. The earliest start time ESTi is calculated
based on the situation that all tasks execute with their best case
number of cycles BNC at the highest voltage setting and low-
est temperature (the ambient temperature). The latest start time
LSTi is calculated as the latest start time of τi that still allows
to satisfy the deadlines for all tasks τj, j ≥ i, when executed with
the worst case number of cycles WNC at the highest voltage and
the maximum temperature Tmax allowed for the chip.
Figure 4: LUT generation
Considering the intended granularity of the LUT, the time and
temperature quanta ?ti and ?Ti are determined. Thus, for task
τi, the number of time entries (the number of different start times
considered) will be (LSTi − ESTi)/ ? ti, while, for each pos-
sible start time, the number of temperature entries is (Tm
Tambient)/ ? Ti, where Tm
ture at the start time of τi. In sections 4.2.2 and 4.2.3 we will
further elaborate on the granularity and size of the LUTs.
When calculating the actual LUT entries for a task τi, the calcu-
lation of the voltage and frequency setting is performed by running
the DVFS algorithm outlined in section 4.1, for all tasks τj, j ≥ i,
considering tsiand Tsias start time and starting temperature,
respectively, for τi.
4.2.2Temperature Bounds and Granularity
As discussed before, the number of entries generated in LUTi
along the temperature dimension is (Tm
basic idea is that the lowest possible temperature is the tempera-
ture of the ambient, while Tm
s iis the highest possible temperature,
in the worst case, at the start time of task τi. But what is the value
perature Tmax at which the chip is allowed to work. While this
assumption is safe, it leads to unnecessarily large tables since, dur-
ing the execution of most of the tasks, the chip will never reach
temperatures close to Tmax. In order to avoid unnecessarily large
tables, we need a safe but tighter upper bound on the temperature
in Fig.4 is executed several times in successive iterations before
the final LUT tables are obtained.
We start by considering that for the first task the maximal start-
ing temperature is the ambient temperature ( Tm
s iis the maximum possible tempera-
s i− Tambient)/ ? Ti. The
s i? One solution is to consider for Tm
s ithe maximum tem-
s i. In order to achieve this goal, our LUT generation algorithm
s 1= Tambient).
The two inner loops in Fig. 4 will generate LUT1. As part of the
DVFS procedure executed during generation of LUT1 we obtain
the possible temperature profiles of τ1and, thus, also the peak tem-
perature Tpeak1reached during execution of this task. The worst
case starting temperature of task τ2 is Tm
this value for Tm
continued for all tasks τi. After the algorithm in Fig. 4 has been
executed once, we have all LUT tables, based on the assumption
that the maximal possible temperature at the start of τ1 is equal
to Tambient. This, however, is not the case, since the application
is executed periodically and τ1 is started again after the last task
τN. Thus, in fact, the maximal staring temperature of τ1 is, in
the worst case, equal to the worst case peak temperature of τN.
Therefore, we repeat the LUT generation algorithm, this time con-
sidering that Tm
in the previous iteration and, thus, a new larger Tm
Thus, new lines will be generated in the LUTs. The procedure is
continued iteratively, until, for a certain task, the peak tempera-
ture over two successive iterations does not change, which means
that no new entries into the LUT tables will be generated. Our ex-
periments have shown that convergence is reached after not more
then 3 iterations. This procedure also allows to detect if there
exists a possibility for the design to reach, in the worst case, a
thermal runaway situation (in which case the iterations do not
converge) or if the maximum allowed temperature can be violated
(there is convergence but there are peak temperatures which are
The above technique leads to a tightening of the range of temper-
atures in the LUT. There are two more questions to be answered
regarding the number of temperature entries (1) What should be
the granularity of the temperature investigation and (2) how to
reduce the number of entries if only a limited amount of memory
It is obvious that a finer granularity and larger number of en-
tries will, potentially, produce better energy savings at the cost,
however, of increased memory consumption. With regard to the
granularity ?Ti, our experiments have shown that values around
15◦C are optimal, in the sense that finer granularities will only
marginally improve energy efficiency. If, due to memory limita-
tions, we only can afford a certain number of temperature entries
NTi to be stored for a task τi, we have to decide which lines of
LUTito preserve and which to eliminate. One straightforward ap-
proach would be to maintain an even distribution of the selected
NTi lines over the range [Tambient,Tm
atures of tasks, during execution, do not spread evenly over this
range. Thus, it is more efficient to have the NTi lines more dense
around the temperature values that are more likely to happen, and
sparse towards the extremes. This means that less pessimistic volt-
age/frequency settings will be used for the most likely cases, while
cases that are much less likely to happen are handled in a more
pessimistic way. Thus, after the LUT tables have been generated,
in order to select the appropriate NTi lines along the temperature
dimension for each task τi, we run a temperature analysis session
in which all tasks are executed for their expected number of cy-
cles ENC. From this analysis, we can observe which is the most
likely starting temperature for each task and we select the NTi
lines among those close to this most likely temperature.
s 2= Tpeak1. Considering
s 2, table LUT2 is generated and the procedure is
s 1= TpeakN. This will lead to a higher Tpeak1than
s 2= Tpeak1.
s i]. However, start temper-
A straightforward approach would be to allocate the same num-
ber of entries, along the time dimension, to each task (Nti is the
same for all tasks τi, i = 1..N). However, the start time inter-
val sizes LSTi− ESTi can differ very much between tasks, which
should be taken into consideration when deciding on the number
of time entries. Therefore, given a total number of entries along
the time dimension NLt, we determine the number of time entries
in each LUTi, as follows2:
LUT Granularity Along the Time Dimension
Nti = NLt∗
The solutions produced by our techniques presented in section
4.1 and 4.2 are safe. By this we mean that:
Accounting for Analysis Accuracy and Ambient
2Let us mention that while the start time intervals are very differ-
ent from task to task, this is much less the case with temperature
interval. Therefore, the number of entries along the temperature
dimension (NTi, see section 4.2.2 has been kept identical for all
tasks in our experiments.
1. It is guaranteed that deadlines are satisfied;
2. If, at run time, a certain frequency setting is selected for a
task τi, it is guaranteed that the temperature during execu-
tion of τi will not exceed the limit allowed for the chip to run
at the selected frequency.
There are two aspects which have to be discussed with respect to
the second of the two statements above. First is the issue of ambi-
ent temperature. If a task τi is starting its execution at a certain
temperature T, the temperature profile during task execution de-
pends on the actual ambient temperature. Thus, a safe frequency
selection has to also take into consideration the current ambient
temperature. Two possible solutions can be considered:
1. Generate the voltage/frequency settings considering the high-
est ambient temperature under which the system is supposed
to function. This is a safe but pessimistic solution with, po-
tentially, smaller energy savings.
2. Generate alternative voltage/frequency settings for a set of
ambient temperatures in the range assumed for the system
to function. During run time, using sensors for the ambient
temperature, the system will switch to those tables corre-
sponding to that ambient temperature that is immediately
higher than the actual measured one. This solution requires
additional memory for storing a larger amount of tables but
could lead to better energy efficiency.
The second aspect to be considered is the accuracy of the tem-
perature analysis. The fact that a certain frequency setting is safe,
with regard to the peak temperature reached during execution of
a task, is based on the temperature analysis performed as part of
the DVFS procedure. Thus, the results can be safe only to the ex-
tent to which this analysis provides safe temperatures. Of course,
system level thermal analysis tools are not provably accurate. Nev-
ertheless, relative precisions are reported for the various analysis
tools and we are using this information in order to account for the
inaccuracy of the thermal analysis. More precisely, given a certain
relative precision of the temperature analysis tool that we use, we
account for this precision in a conservative way when determining
the peak temperatures used for frequency calculation.
In section 5, we will evaluate the impact of both ambient temper-
ature and potential analysis inaccuracy on the energy optimization
5. EXPERIMENTAL RESULTS
Our experiments have been performed on both generated appli-
cations and a real-life example.
We have randomly generated applications consisting of 2 to 50
tasks. The WNC of the tasks are in the range [106,107]. The
applications are executed on a processor which can run at 9 differ-
ent supply voltage levels in the range [1.0, 1.8]. The temperature
model related coefficients are the same as in , while the power
model related coefficients are as in
, in equ.4 we use the coefficients μ = 1.19, ξ = 1.2, and k
= -1.0V/◦C. If not mentioned differently we assume an ambient
temperature of 40◦C.
It is important to mention that in all our experiments, we have
accounted for the time and energy overhead produced by the on-
line component of our dynamic approach. Similarly, we have also
taken into consideration the energy overhead due to the memories.
This overhead has been calculated based on the energy values given
in  and .
The first set of experiments is aimed at evaluating the efficiency
of taking into consideration the frequency/temperature depen-
dency. We first compare the static DVFS approach in , which
ignores the frequency/temperature dependency, to our static ap-
proach proposed in section 4.1. Considering the average over all
25 applications the energy consumption obtained by considering
frequency/temperature dependency is 22% smaller than when ig-
noring this dependency.
For the dynamic approach, described in section 4.2, we have run
the same set of experiments, first ignoring frequency/temperature
dependency and than considering it. The energy consumption has
been reduced, on average, by 17% in the latter case.
The next set of experiments compares the energy consumption
with the static DVFS approach in section 4.1 and the dynamic
one in section 4.2 (both considering frequency/temperature de-
pendency). As the ratio BCN/WCN has a strong influence on
the potential efficiency of a dynamic approach, we run the experi-
ments considering three different ratios: 20%, 50%, and 70%. Also,
we assume that the workload distribution of each task conforms
.Similar to  and
to a normal distribution N(ENC,σ2), where ENC is the mean
value, and σ is the standard deviation. For our energy evaluations
we have generated actual numbers of executed clock cycles for
each task considering standard deviations of (WNC − BNC)/3,
(WNC−BNC)/5, (WNC−BNC)/10, and (WNC−BNC)/100.
Fig.5 shows the energy savings with the dynamic approach rela-
tive to the static one. As can be observed, the efficiency of the
dynamic approach, compared to the static one, increases as the
ratio between BNC and WNC becomes smaller. Remember that
our DVFS algorithm is targeted torwards optimizing the energy
consumption for the case that tasks execute the expected nunm-
ber of cycles ENC. Therefore, energy savings are larger, compared
to the static approach, when the standard deviation σ is smaller
(more of the actual executed number of clock cycles are clustering
around the ENC).
BNC / WNC=0.7BNC / WNC=0.5BNC / WNC=0.2
Figure 5: Dynamic vs Static Approach
The third set of experiments is aimed at exploring the impact of
the LUT sizes. In particular, for this paper, we are interested in
the impact of entries along the temperature dimension. The num-
ber of lines along the time dimension has been kept constant for
these experiments and is distributed according to the discussion in
section 4.2.3. First we run, for all applications, our dynamic DVFS
approach considering a granularity ?T = 10◦. We evaluate the av-
erage energy reduction with the obtained LUTs, compared to the
static approach. Then we impose a certain limitation on the num-
ber of lines along the temperature dimension and we construct the
corresponding LUTs as discussed in section 4.2.2. We again evalu-
ate the energy consumption considering these reduced LUTs. The
diagram in Fig. 6 shows the average results for different number
of lines and two different standard deviations of the actual number
of clock cycles executed by tasks. Having one single temperature
entry will produce energy reductions compared to the static case
which are 37% smaller (for σ = (WNC − BMC)/3) than with an
unreduced LUT. However, with 2 entries the results are already
very close to those obtained with an unreduced LUT and with 3
entries they are, in practice, identical. This is good news, since
it shows that significant energy savings can be obtained with rel-
atively small memory overhead. It should be mentioned that all
other experiments presented in this section have been performed
with 2 entries along the temperature dimension.
Penalty on Energy Efficiency %
Figure 6: Impact of Temperature Line Number
Our final experiments have been performed in order to explore
the impact of ambient temperature and temperature analysis ac-
curacy. For all experiments above, we have assumed that Tambient
is 40◦C and is known at design time. In order to evaluate the
impact of the ambient, we considered all the generated applica-
tions and constructed LUTs for values of Tambient in the range
[-10◦C, 40◦C]. For each (application, LUTs) pair corresponding to
a certain Tambient we evaluated the energy consumption consider-
ing that the Tambient is identical with the one assumed at LUT
generation. Then we run the simulations for the same (applica-
tion, LUTs) pair, but considering that Tambient deviates with 10◦,
20◦,...,50◦from the value assumed at design time. The results are
shown in Fig.7. We can see that if Tambient is different by, for
example, 20◦from the one assumed at design time, the energy
consumption increases by only 7% on average. This shows that, if
the predicted range of ambient temperature is, for example, 40◦,
generating two sets of LUTs (granularity of 20◦) will lead to energy
losses, on average, less than 7%.
0 10 2030 4050
Energy Penalty %
Figure 7: Impact of the Ambient Temperature
All the presented experiments have been performed considering
that the temperature modeling and analysis is accurate. We have
repeated the experiments considering a relative accuracy of 85%.
When calculating frequency settings we accounted, in a conserva-
tive way, for this degree of accuracy. Our experiments have shown
that the energy degradation due to the 85% relative accuracy is
less than 3%.
Finally, we apply our static and dynamic approach described
in section 4 to a real life case, namely an MPEG2 decoder which
consists of 34 tasks and is described in more detail in . The
energy consumption using the static approach is reduced by 22%
when considering frequency/tempeature dependency. For the dy-
namic approach the reduction is 19%.
quency/temperature dependency, the dynanmic approach gives an
energy reduction of 39% compared to the static one.
When considering fre-
We have proposed an on-line temperature aware dynamic volt-
age and frequency scaling (DVFS) technique which is able to ex-
ploit both static and dynamic slack and takes into consideration
the frequency/temperature dependency. The approach consists of
two parts: an off-line temperature aware optimization step and on-
line voltage/frequency setting based on temperature sensor read-
ings. Experiments show that significant energy improvements can
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