Statistical Usage Models in Mobile Processor Thermal Design and Testing
by
Thomas C. Evans
B.S. Mechanical Engineering, Case Western Reserve University 1994
Submitted to the Department of Mechanical Engineering and the Sloan School of
Management in Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Mechanical Engineering and
Master of Business Administration
In Conjunction with the Leaders for Manufacturing Program at the
Massachusetts Institute of Technology
June 2003
@2003 Massachusetts Institute of Technology. All rights reserved.
Signature of Author
Department of Mechanical Engineering
Sloan School of Management
May 2003
Certified by
Daniel E. Whitney
Senior Research Scientist
Thesis Supervisor
Certified by___________
C
edRoy
E. Welsch
Professor fStatistics and Management Science
Thesis Supervisor
Accepted by
Ain Sonin, Chairman, Graduate Committee
Department of Mechanical Engineering
Accepted by
Margaret Andrews, Executive Director of Masters Program
Sloan School of Management
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
JUL 0 8 2003
LIBRARIES
2
Statistical Usage Models in Mobile Processor Thermal Design and Testing
by
Thomas C. Evans
Submitted to the Department of Mechanical Engineering and the Sloan School of
Management on May 9, 2003 in partial fulfillment of the Requirements for the Degrees
of Master of Science in Mechanical Engineering and Master of Business Administration
Abstract
The performance, quality and reliability of microprocessors are highly dependant upon
their operating temperature. Intel microprocessors are equipped with a thermal monitor
feature that reduces power to the chip when the maximum temperature is reached to
prevent overheating. This is commonly referred to as "throttling". Worst-case thermal
design methodologies provide highly robust thermal solutions that keep processors
running below their maximum temperature. OEMs do not always follow Intel's
recommendations in this regard, particularly when they use desktop processors in mobile
form factors. The processors in these systems run hotter and are more likely to throttle.
A methodology that uses the principles of statistical tolerancing is developed to quantify
the performance impact of throttling on thermally under-designed mobile systems.
Customer usage models are developed from market survey data, and used with Monte
Carlo simulation techniques to calculate the distributions of processor temperature and
performance in use. Simulation results from both worst-case operating conditions and a
statistical usage model are analyzed and compared. The statistical usage model is then
used to compare the theoretical operation and performance of a true mobile system and a
desktop processor transportable system. Finally, an analysis is performed to evaluate the
effect of reducing the throttle set point on a mobile processor.
Results show that the predicted occurrences of throttling drops by two orders of
magnitude when comparing the statistical usage model to worst-case, that approximately
10 percent of the desktop transportable systems would throttle severely, and that total
quality events could be reduced by 37 percent by lowering the throttle set point.
The implications for new product positioning and increasing integrality of mobile
computer systems are discussed.
Thesis Supervisor: Daniel E. Whitney
Title: Senior Research Scientist
Thesis Supervisor: Roy E. Welsch
Title: Professor of Statistics and Management Science
3
Acknowledgements
I would like to acknowledge the Leaders for Manufacturing Program for its support of
this work.
I would like to acknowledge Vivek Phanse, my supervisor at Intel, for his support and
guidance throughout my internship.
I would like to acknowledge Professors Whitney and Welsch for their constructive ideas.
I dedicate this thesis to my wife, Nancy, without her constant support and encouragement
none of my success would have been possible.
4
Table of Contents
A bstra ct ..............................................................................................................................
Acknowledgements.......................................................................................................
Table of Contents.........................................................................................................
Chapter 1 - Overview ..................................................................................................
1.1 Background ........................................................................................................
1.2 Project Goals.......................................................................................................
1.3 Approach..............................................................................................................
Chapter 2 - Background and Project Setting ........................................................
2.1 Statistical Tolerancing......................................................................................
2.2 Intel Mobile Platform Group ...............................................................................
2.3 Thermal Enabling .............................................................................................
2.3.1 Thermal Design Power............................................................................
2.3.2 TDP Ratio and Application Ratio (AR)...................................................
Chapter 3 - Product Overview .................................................................................
3.1 Thermal Monitor................................................................................................
3.2 Thermal Impact ................................................................................................
3.2.1 Temperature Effect on Processor Frequency.......................................
3.2.2 Temperature Effect on Processor Reliability .......................................
3.3
Power Consumption ...................................................................................
3.3.1 Dynamic Power.........................................................................................
3.3.2 Leakage Current Power .........................................................................
3.4 Heat Dissipation................................................................................................
Chapter 4 - Simulation Process ..............................................................................
4.1 Monte Carlo Simulator ....................................................................................
4.2 Simulator Modifications....................................................................................
4.2.1 Application Ratio and Ambient Temperature Distribution...................
4.2.2 Performance Impact Modeling ...............................................................
Chapter 5 - Usage Models.........................................................................................
5.1 Mobile Choice Survey......................................................................................
5.2 Defining a "User"..............................................................................................
5.2.1 Software Characterization - Application Ratio ....................................
5.2.2 User Characterization...............................................................................
5.3 Usage Model Matrix ........................................................................................
5.3.1 Worst-Case Peak 5-Second Average Usage Model ..........................
5.3.2 Expected Peak 5-Second Average Usage Model................................
5.3.3 Expected Average Usage Model..........................................................
5.3.4 W orst-Case Average Usage Model......................................................
5.4 Application Ratio Distribution Parameters ...................................................
5.5 Ambient Temperature Distributions.............................................................
Chapter 6 - Simulation Results ..............................................................................
6.1 Worst-Case Operating Conditions vs. Statistical Usage Model...............
5
3
4
5
7
7
8
8
11
11
13
15
16
17
19
19
20
20
22
22
23
24
24
27
27
30
30
31
33
33
34
35
36
37
37
39
41
43
44
47
49
49
6.2 Mobile System vs. Desktop Arbitrage System...........................................
6.3 Recommended Change to Throttle Set-Point..............................................
Chapter 7 - Recommendations and Future Use ..................................................
7 .1 V a lid atio n ........................................................................................................
..
7.2 Market Segments and Product Positioning ..................................................
7.3 Industry Dynamics and Strategic Partnerships...........................................
Appendix A: Selected Questions and Sample Answers from End-User Mobile
C ho ice S u rvey.............................................................................................................
.
B ib lio g ra p h y.....................................................................................................................
6
54
60
69
69
71
73
75
77
Chapter 1 - Overview
1.1 Background
Intel Corporation is the leading supplier of microprocessors to the computing industry.
Intel supplies microprocessors to many market segments, including both the desktop
market segment and the mobile market segment. The appearance of a new mobile
computing market segment, the desktop replacement or "transportable", has resulted in
higher-power desktop processors installed in laptop form factor chassis. The high power
processors are pushing the limits of the cooling systems that can be used in these form
factors, and processors are running hotter.
The performance, quality and reliability of microprocessors are highly dependant upon
their operating temperature. Intel microprocessors are equipped with a thermal monitor
feature that reduces power to the chip when the maximum temperature is reached to
prevent overheating. This is commonly referred to as "throttling".
Intel uses statistical methods and tolerances for many aspects of estimating product
quality and to determine product-testing specifications as part of its standard business
practice. However, the usage model input, one of the major factors, is typically entered
as a "worst-case realistic" value. The thermal design process is also based on worst-case
tolerances. Intel sets its thermal specifications and recommendations such that processor
"throttling" is an unlikely event, happening only under extreme circumstances. However,
7
some original equipment manufacturers (OEM) are disregarding Intel's
recommendations, and are producing systems with less capable thermal solutions. These
systems are considered thermally under-designed, and throttle more often because the
processors are not adequately cooled and running hotter. With increases in processing
power, thermal under-design is expected to increase in both quantity and magnitude. The
problem that Intel is faced with is the challenge of realistically predicting or simulating
the amount of throttling that occurs in the under-designed systems.
1.2 Project Goals
The project goals are to answer the following questions:
*
What is the expected occurrence of throttling with thermally under-designed
systems?
o
How many units will throttle/suffer performance impact?
o
How significant is the performance impact?
o
How often is the performance impacted?
" How can Intel incorporate the usage model of mobile computers into product
specs and recommendations and/or process parameters in order to maximize
value?
1.3 Approach
Chapter 2 begins with a brief description of statistical tolerancing and a comparison to
worst-case tolerancing. It then looks at the project setting, the mobile computer market
segment, and the Thermal Enabling function at Intel. Chapter 3 goes into some depth
8
about the microprocessor product characteristics that are germane to this thesis. Chapter
4 discusses the simulation process that is the basis for making the throttling predictions.
Chapter 5 develops the statistical usage model that is used as an input to the simulator to
provide realistic throttling estimations. Chapter 6 reviews the results of the three
simulation comparisons: worst-case operating conditions vs. statistical usage model,
mobile system vs. desktop arbitrage system, and 100 *C vs. 95 'C throttle set point.
Chapter 7 concludes with recommendations and future uses for the methodology
developed.
9
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10
Chapter 2 - Background and Project Setting
2.1 Statistical Tolerancing
Statistical tolerancing is a methodology that allows an increase in the manufacturability
of a product while achieving the required performance and quality by understanding the
statistical nature of the factor deviations. Statistical tolerancing works because the
chance that all factors will experience maximum deviations in the same direction, what is
called worst-case stack-up, is small. In fact, with more factors involved the chance of
worst-case stack-up typically becomes less. This is in direct contrast to worst-case
tolerancing, which always assumes worst-case stack-up. As a simple example, imagine a
five-inch stack of blocks made of five one-inch blocks as shown in Figure 2.1.
1.000" ±/-?
5.000" +/- 0.050
Figure 2.1: Example of stack-up tolerances
If the tolerance for the stack is 0.050 inches, what should the tolerance on each individual
block be? Worst-case tolerancing says 0.010 inches. That way sum of the tolerances can
never be more than 0.050. If the size of the blocks is normally distributed with a mean of
11
one and standard deviation of 0.005 (i.e. N(l, 0.005)), then five percent of the blocks
would be outside of the 0.010 individual tolerance and would need to be scrapped.
However if all of the blocks are N(1,0.005) and the deviations are independent, then the
stack can be assembled with all of the blocks (none of the blocks are scrapped), and only
one in 100,000 of the assembled stacks will fall outside of the 0.050 stack tolerance. This
is because the chance that all five blocks will have large deviations in the same direction
is rare. This is the principle that makes statistical tolerancing work. One caveat of
statistical tolerancing is that there is no longer any absolutes. One cannot say that the
stack will never be greater than X. There is always some probability, however
infinitesimal that it could happen. Of course, it is still possible to inspect the assembly
and scrap and assemblies that do not meet the final specifications.
Intel uses this kind of statistical approach to estimate product quality and to determine
product-testing specifications as part of its standard business practice. However, the
usage model input, one of the major factors which contains two input variables, is
typically entered as a "worst-case realistic" value.
Let us return to the block example and see what happens if we assume that two of the
blocks are always at the worst-case tolerance of 0.010, even though they are still actually
N(1,0.005). We now need to inspect the assembly to 3 inches +/- 0.030 after stacking the
three blocks we know to be normally distributed. Now 30 in 100,000 three-block
assemblies would measure out of tolerance and need to be scrapped. The scrap rate
12
increased by a factor of 30 because-worst case assumptions were made for two blocks out
of five. This is analogous to the approach Intel is taking with its quality simulations, and
why it is important to correctly identify the distributions for the last two parameters.
2.2 Intel Mobile Platform Group
Intel Mobile Platform Group (MPG) is one of Intel's six strategic business units. MPG
and is responsible for developing and marketing microprocessors, chipsets and related
hardware and software for laptop and other mobile computing systems. Products for this
market segment have features not found in desktop products. These features, most of
which revolve around reducing power consumption; make them particularly valuable for
mobile applications. For this reason, mobile products command a premium in the
market.
Over the last several years the mobile market segment has become much more
complicated as the traditional notebook computer form factor has given way to a variety
of form factors as shown in Figure 2.2
13
Transportable
=MWW9:::f
V7
Notebook
computer
Thin &
light
MiniNote
Figure 2.2: The variety of mobile form factors currently available.
Source: Intel Mobile Platforms Group
In addition to the proliferation of form factors, mobile system powers have been
increasing rapidly, as shown in Figure 2.3.
System Power
60
50
40
30
n"20
10
0
1994
1995
1996
1997
1998
1999
2000
Figure 2.3: Average total mobile system powers.
Source: Intel Mobile Thermal Enabling
14
2001
The active/passive line refers to the cooling threshold described in Chapter 3. The
combination of smaller form factors and higher system powers continues to make heat
removal more challenging. In addition, OEM's have been installing higher-power
desktop processors in laptop form factor chassis, creating a new mobile computing
market segment, the desktop replacement or "transportable". This practice is referred to
as "desktop arbitrage", as desktop processors are replacing premium priced mobile
processors in what is traditionally thought of as a mobile form factor.
Obviously, "desktop arbitrage" is of great concern to MPG as they are directly losing
sales of its mobile products. It also negatively impacts the revenue of Intel as a whole as
sales of less expensive desktop processors replace those of the premium priced mobile
processors. In addition to the negative financial implications for Intel, "desktop
arbitrage" also presents significant technical hurdles. The higher power processors
exceed the capabilities of the cooling systems used in these mobile form factors.
2.3 Thermal Enabling
The role of the Mobile Thermal Enabling team is to provide guidance and make it as easy
as possible for customers (OEMs) to design Intel products into its systems. They test
solutions from suppliers and create example thermal solutions. They also publish design
collateral on the best design practices. Most interesting for this thesis is the first part of
the thermal enabling process; defining the Thermal Design Power (TDP) for a processor.
15
2.3.1 Thermal Design Power
TDP is the value that Intel recommends the computer OEMs use in designing their
cooling solutions. TDP is not the theoretical maximum power for a given chip design,
but is an empirical value (adjusted for anticipated leakage) derived from testing the
processor with most intensive commercially available software application. A wide
variety of software applications are screened to find the one that is most processor
intensive.
During TDP testing the power consumed by the processor is measured and recorded at
the millisecond timescale. However power spikes at that frequency are of no thermal
interest. Due to the thermal inertia of the processor and the cooling solution the
temperature of the processor responds on a much slower time scale. Therefore the
maximum power of interest for a given application is not the maximum power spike.
Laboratory experiments, computer modeling and experience have shown that a rolling 5second average power trace is a good indicator of the temperature that a mobile processor
will reach. The rolling 5-second average power is post-processed from the data. Each
data point is given the average value of the previous five seconds worth of data. This
simulated the thermal inertia of the system. Since the TDP value is intended for thermal
calculations it makes sense to use a measurement that corresponds with temperature.
Because the maximum temperature that the processor reaches is the value of interest, the
peak 5-second average is used as the rating. Figure 2.4 shows a power trace for a
software application test.
16
3Dmark2000
1.6
1.4
1.2
0
0.8
0.6
0.4
In
(C0
r-
(a C
DC
40
0)0NN
C
0
v
N
.0
D
C
-N
C
U
N
Time
Figure 2.4: Software application power trace showing power spikes, 5-second rolling
average power, and mean power. Source: Intel Mobile Power Lab
After the peak 5-second average value is calculated the TDP rating still needs to be
corrected for leakage power. Leakage power varies from chip to chip (see Section 3.3.2).
The leakage power is measured for the TDP test chip when it is at idle. To make the
correction the test leakage value is subtracted from the measured peak 5-second average
power and the maximum leakage power for that processor specification is added back.
This ensures that the TDP corresponds to the highest leakage part for a given
specification.
2.3.2 TDP Ratio and Application Ratio (AR)
TDP is expressed in watts. However it can also be useful to think of it in terms of a ratio
of a maximum power. The maximum power of a processor is determined by running a
"power virus", a piece of specifically developed in-house software code that has no
17
functionality other than to apply maximum stress to the CPU. The TDP ratio is then
defined as shown in Equation. 2.1.
TDP ratio
=
(TDP - Leakage power)
(Maximum power - Leakage power)
(Eq. 2.1)
TDP ratios typically fall between 0.7 - 0.85. The TDP ratio can be particularly useful in
modeling simulations and in comparing processors. Software applications can also be
defined in terms of an application ratio (AR). In mobile computing the process is the
same as defining the TDP ratio. The equation 2.2 is given for AR.
AR =
(Peak 5-second average power - Leakage power)
(Maximum power - Leakage power)
(Eq. 2.2)
This chapter looks at the fundamentals of statistical tolerancing, provides an overview of
Intel Mobile Platform Group and gives some insight to the functions of the Mobile
Thermal Enabling team. The next chapter focuses on the features and characteristics of
Intel microprocessors that are important for this thesis.
18
Chapter 3 - Product Overview
The central feature of the Intel microprocessor that is relevant to this work is the thermal
monitor. The thermal monitor attempts to control the processor temperature by initiating
the thermal control circuit, which reduces the processor frequency, and hence its power
dissipation. This is commonly referred to as "throttling". This chapter will provide a
brief discussion of the thermal monitor, why it is necessary, how heat is generated in a
semiconductor, and how that heat is removed from a mobile computer system.
3.1 Thermal Monitor
Intel® Pentium® 4 processors are equipped with a thermal monitor feature. This consists
of an on-die temperature sensor and a built in control circuit which can stop the processor
clock input to the central processing unit (CPU) core, placing the microprocessor in a
lower power state [1]. When the microprocessor reaches the maximum operating
temperature (the Throttle Set Point) the thermal monitor toggles the stop clock feature.
Modulating the power dissipated in the CPU reduces it linearly with the percentage of
time the clock is stopped, and preventing the processor from overheating. While the
clock is stopped the CPU is also prevented from executing instructions, which reduces its
operating performance. This is commonly referred to as "throttling".
Based on the characteristics described above, this project set out to answer 3 questions:
1.
How many Mobile CPU's will throttle and suffer performance impact?
19
2. How significant is the performance impact?
3. How often does the performance impact occur?
3.2 Thermal Impact
Why is a thermal monitor necessary? The thermal monitor is designed to provide nontraumatic temporary performance decreases that are virtually unnoticeable to the end user
in the place of more severe thermal failures. Without a thermal monitor a microprocessor
can literally overheat and destroy itself [2]. Temperature affects the operation of a
microprocessor in several ways. A microprocessor's maximum frequency is a function
of its temperature. Changes in temperature can have an instantaneous effect on how fast
the processor can perform operations. Temperature also has a cumulative impact. The
long-term reliability of a microprocessor is also a function of its temperature. Elevated
temperatures increase the rate of all chip related failure mechanisms [3]. These two
temperature effects are discussed below. The purpose of the thermal monitor is to
substitute short-term performance losses in place of more severe thermal failures.
3.2.1 Temperature Effect on Processor Frequency
The maximum frequency that a microprocessor can operate at before it loses functionality
is referred to as Fmax [4]. Fmax is a function of both input voltage and temperature.
Fmax increases with increasing voltage, and decreases with increasing temperature.
Fmax can be measured on an individual part either in the laboratory or on the production
line with the proper equipment. Due to manufacturing variation there is a distribution of
Fmax on any given product. An example of this distribution is shown in Figure 3.2.
20
(0
.
a-
Fmax (GHz)
Figure 3.2: Processor frequency maximum (Fmax) due to manufacturing variation.
Source: Intel Corporation
Before a device is shipped its frequency is tested at a prescribed temperature and voltage
at the factory. Based on the results of this test the device is "binned", and its speed is
preset. If a microprocessor's temperature increases (or voltage decreases) to the point
that Fmax is less than the pre-programmed frequency, the processor will "lock-up" and
stop operating. This is known as an Fmax failure. The speed is preset on each device
such that as long as the temperature specifications are met, an Fmax failure would be an
extremely rare event. Throttling is a mechanism that is intended to prevent a catastrophic
Fmax failure by virtue of trying to limit the temperature.
21
3.2.2 Temperature Effect on Processor Reliability
In addition to the instantaneous Fmax failures, elevated temperatures also have a
cumulative negative effect on the long-term reliability of a semiconductor
microprocessor.
All chip related failure mechanisms are aggravated by high temperature. One example is
dopant diffusion, which is the diffusion of contaminants within the semiconductor
structure. Increases in temperature increase the diffusion rate, which in turn reduces the
mean time to fail. Another example is gate oxide, where temperature increases the rate of
the oxidation reaction [3]. In addition, a large number of assembly related and operation
induced failure mechanisms are also aggravated by high temperature. One example is
electro migration, an operation induced failure where higher temperatures make it easier
for metal atoms to be moved by the impact of current flow [3]. These effects also make it
desirable to limit the silicon temperature.
3.3
Power Consumption
Heat is generated in a semi-conductor circuit, like any electrical circuit, when a voltage is
applied across a resistance and a current flows. The heat generated is equal to the power
consumed. For a simple approximation, the power consumed by a digital logic circuit is
entirely due to charging and discharging circuit capacitor nodes [5]. The power
consumed by any node is small, but multiplied by millions of transistors per chip
22
switching billions of times per second, it adds up rapidly [1]. This results in Equation 3.1
for power:
Power
= CtotaIV 2 f
(Eq. 3.1)
where: Ctotai is the average capacitance charged per cycle
V is the supply voltage
f is the operating frequency.
Equation 3.1 only accounts for the dynamic power that occurs from changes in transistor
states. There is also a leakage current power component that in theory should be zero,
but has become more significant as processor geometries have shrunk and incorporated
more gates [1]. Both dynamic power and leakage current power are detailed below.
3.3.1 Dynamic Power
Dynamic power is typically the major source of power dissipation in microprocessors [6].
The dynamic power consists of the dynamic switching power and short circuit current
power. The dynamic switching power is the power consumed in switching a transistor
gate, and the short-circuit current occurs during a transition when both the input and
output gates are partially open. In a well-designed circuit the short circuit power
dissipation can be limited to 5-10% of the total dynamic power [6]. For this thesis the
most important aspects of dynamic power are that it is the dominant source of power
dissipation in microprocessors and it is determined by the design of the microprocessor
(the number of transistors and their capacitance), the operations it is performing (how
many transistors are switching), the supply voltage, and operating frequency.
It is also useful to think in terms of the dynamic current, which is the dynamic power
divided by the supply voltage.
23
3.3.2 Leakage Current Power
The leakage, or standby current (Isb) is, as the name implies, the current that leaks across
transistors when they are turned off Therefore leakage current is independent of the
operations performed. Leakage current is temperature sensitive, Isb can increase
dramatically at higher temperatures [6]. As a result of manufacturing variability the Isb
can vary significantly. The distribution of Isb for a particular product will also change
over time as the manufacturing process matures. Also, there are process parameters that
can be used to tune other desirable properties of the microprocessors that also affect
leakage current, and will add to the shifts in Isb distribution. For each product there is a
maximum specified Isb, and all parts are screened to below that level. The leakage
current power is calculated with ohm's law:
Pleakage = V *
Isb
(Eq 3.2)
3.4 Heat Dissipation
All of the heat generated within a system needs to be dissipated, otherwise the
temperature will continue to rise. A normally sized mobile computer can accommodate
approximately 15 W of passive cooling [7] from radiation and natural convection.
Systems that exceed this total require an active cooling solution. In particular, the CPU,
which has been on of the higher power devices in the system, requires a dedicated active
thermal solution. A remote heat exchanger (RHE), as depicted in Figure 3.3, is the most
common technology used to remove heat from the CPU in mobile computer systems.
24
Attachment
Block
Air Inlet
M
Air
Exhaust
Heat Pipe
CPU
(junction)
Heat
Exchanger
(HX)
Figure 3.3[8]: Remote heat exchanger for mobile computer systems.
The heat generated at the transistors in the processor is conducted through the silicon into
the attachment block. There is typically a thermal interface material between the CPU
and the attachment block to improve conduction across this boundary. The attachment
block also serves as a heat spreader to equalize temperatures across the silicon die. The
heat then flows through the heat pipe. Heat pipes are extremely efficient. They operate
by vapor/liquid phase change and mass transfer [7]. The heat is then conducted into the
fins of the heat exchanger, where it is removed via forced convection from the fan.
The heat flow path can be modeled as a series of thermal resistors as shown in Figure 3.4.
0
j-heat pipe
0
heat pipe-HX
OHX-ambient
Oj-a
Figure 3.4: Thermal resistance model of remote heat exchanger
25
The total thermal resistance is empirically defined in a steady state condition as shown in
Equation 3.3.
(Eq. 3.3)
E_ 'i Po
we
Power
where
0
j-a is the thermal resistance in 0 C/W
Tj is the transistor junction temperature of the CPU in 'C
Ta
is the ambient temperature in 'C
Power is the power dissipated by the CPU in watts
Lower thermal resistance allows greater cooling capacity for a given temperature
difference. To reduce the total thermal resistance it is necessary to reduce one or more of
the resistances in series. The most common method to reduce
Oj-heat
pipe is to use a lower
resistance thermal interface material. The heat pipe thermal resistance can be reduced
with a larger cross-section heat pipe. The easiest way to reduce the total resistance is to
reduce OHX-ambient by using a heat exchanger with greater surface area, a larger more
powerful fan to flow more air, or both. However, those options are in direct contradiction
to the desire to make the systems smaller, thinner, and lighter.
This chapter reviews the thermal monitor feature of Intel microprocessors and the thermal
impact and power consumption characteristics of semi-conductor microprocessors. The
next chapter looks at how those characteristics and features are a simulator for product
quality.
26
Chapter 4 - Simulation Process
The simulation tool used as the basis of this work was developed by Intel Corporation.
The simulation runs within Excel (Microsoft Corp.) and uses JMP (SAS Institute Inc.) for
data analysis and graphing. The tool is used to understand the impact of different test
conditions for microprocessors and how they affect factory yield and quality defects in
the field. Quality defects are defined broadly as when a device does not perform as
expected. Setting the test conditions too conservatively results in unnecessarily scrapping
microprocessors at the factory that would have performed flawlessly in the field. Setting
the test conditions too aggressively increases the yield at the factory, but will result in
quality defects in the field. This chapter provides an overview of how the simulator
works at a very high level, and the modifications that where made to it.
4.1 Monte Carlo Simulator
In Monte Carlo simulations, a model is evaluated repeatedly using parameter values that
are randomly drawn from statistical distributions. The results of the repeated simulations
are then evaluated in terms of their statistical distributions. Monte Carlo methods are
particularly useful when dealing with complex systems and a variety of failure rate
models [9].
27
The tool used in this project simulates the microprocessor product creation, testing, and
end use in a virtual environment. The simulator uses the Monte Carlo technique to
randomly sample the products, testers and end use conditions from a statistical
description of the characteristics of each. Millions of parts can be sampled, tested and
evaluated under use conditions in this virtual environment leading to estimates of failure
rates in defects per million (DPM). By treating parts as samples of a distribution, worst
case assumptions can be avoided and more accurate assessments of expected field failure
rates are obtained.
First, the product is created by randomly choosing parameter values for Fmax, Dynamic
current, and Isb. Distributions for those parameters for a product are developed from
production data and engineering characterization data. The randomly chosen parameters
are fed into a set of equations that define the operating characteristics of the
microprocessor.
After the program creates a simulated product it is virtually tested by choosing a set of
parameters that define the test environment. The automated test equipment (ATE) is
described by its operating tolerances and the test condition settings. Statistics and
distributions of the product characteristics that pass the testing can be calculated.
If the device under test passes, it is then simulated in the end-use environment. The enduse environment is characterized by tolerances for the electrical and thermal
specifications of the system platform or chassis, the ambient air temperature, and degree
28
to which the microprocessor is utilized or stressed. Statistics and distributions of the
product characteristics in the field can be calculated. Figure 4.1 is a diagram showing an
overview of the simulation.
Product
0 tibtoteristicC
A TE,
ttings and
Ed
s
Producer iImpact
Figure 4.1: Simulation overview. Source: Intel Corporation
This work focuses on the thermal aspects of the End-Use Characteristics and the
Customer Impact. From this perspective the most important value that the simulator
calculates is the temperature of the microprocessor (Tj) in use as defined in Equation 4.1.
Tj
=
Tair + Tsys + O*Power
(Eq. 4.1)
where: Tair is the ambient temperature that the system is operating in.
Tsys is the temperature rise within the system due to other electrical components.
o is the thermal resistance of the OEM thermal solution in *C/W.
Power = V*(Isb + AR*Idyn)
Isb is the leakage or standby current
AR is the software application ratio
Idyn is the dynamic current
The above equations have been simplified for presentation here. Temperature, current
and voltage are all coupled in the simulation model.
29
4.2 Simulator Modifications
Several comments need to be made about the simulator methodology. The methodology
is not concerned with behavior integrated over time. It mainly focuses on instantaneous
catastrophic events such as Fmax. Therefore, no time dimension is included in the
model. For inputs that will vary over the lifetime of a device, such as software
application ratio, and in the case of mobile computers, the ambient environment, the
methodology is only concerned with the worst-case instances that may lead to a failure.
For these reasons, the application ratio is set at the TDP ratio and the ambient
temperature is set at 35 0 C.
However, throttling is not an instantaneous catastrophic event. It can be a reoccurring
event that happens frequently over the lifetime of a device. One of the goals of this
project is to determine how often throttling will occur for a particular device. This
explicitly requires some inclusion of a time or frequency dimension. The application
ratio and ambient temperature variables are used to bring that time dimension into the
simulator.
4.2.1 Application Ratio and Ambient Temperature Distribution
With assistance from the programmers at Intel the simulation code was modified to
accept statistical distribution parameters for the application ratio that are sampled by the
Monte Carlo method. The application ratio parameters are developed to include an
estimation of the amount of time spent along each point on the curve (see Chapter 5).
30
The simulator already had the capability to sample from a statistical bi-modal ambient
temperature distribution. A time-based model of this distribution for mobile computers is
developed in Chapter 5.
4.2.2 Performance Impact Modeling
Another goal of this project is to estimate the performance impact of throttling. When the
simulator calculates Tj for a device that is greater than the throttle set point temperature,
it then back calculates the AR required to maintain the maximum temperature for the
device. This reduction in AR is translated into a performance loss based on performance
data collected in the lab. The problem was that all of the performance loss data was
focused on the TDP application ratio. With the application ratio distribution model,
information about performance loss from a much greater range of applications is
required.
Lab testing of a broad range of software benchmarks under throttling conditions showed
consistent performance decreases for similar AR reductions. A model is fit to this
performance data. The model, shown in Figure 4.2, is exponential and is included as a
post processor in JMP.
31
(0
-j
E
(0
AR Reduction
Figure 4.2: Performance loss model.
The most important attributes of Figure 4.2 are that for small levels of AR reduction
(throttling) the performance loss is negligible, and that there is a rapid transition from
small performance loss to very significant performance loss. The implications are three
regimes: limited throttling produces almost no performance impact; throttling can
produce moderate performance decrease and the system will still function; performance
loss increases rapidly to the point where system functionality may be compromised.
This chapter looks at the framework of a product quality simulator that uses Monte Carlo
methods, and how to incorporate a time dimension into the simulation by using statistical
usage models. The next chapter looks at the development of the user models in detail.
32
Chapter 5 - Usage Models
In order to predict the quality of a product some determination of its intended use has to
be established. These are termed usage models and typically contain information
regarding how the product is used, how often and for how long, and under what
conditions. Software benchmark companies have conducted a significant amount of
research and data collection on how people use software [10,11]. This thesis will build
on that work and focus on which software applications are being used, processing power
those applications utilize, and the environment and ambient temperature in which the
computer is being used.
5.1 Mobile Choice Survey
The basis for this usage model was data from a survey of mobile computer end users that
Intel Corporate Marketing Research had conducted in the spring of 2002. Over 900 users
were surveyed from multiple countries across the globe. Embedded in the survey were
five questions that related to the manner in which people used their mobile computers.
These questions included:
What environments?
Hours per week on?
Hours per week from batteries?
Hours per week actively using?
33
What type of activities(software applications)?
Appendix A contains the actual survey questions as well as sample tabulated answers.
Another useful feature of the survey data is that it included sample-weighting parameters.
These are used to more accurately aggregate results among various sub-groups. For
example, consider 200 users surveyed in two different countries. If one country has a
total user population of 10,000,000 users and the other country only has 100,000 users,
simply combining the survey results would not provide an accurate picture of the entire
10,100,000-user population. Differences in the ratios between the number of users
surveyed in a subpopulation and the total number of users in a sub-population are
accounted for by the sample weighting parameters.
5.2 Defining a "User"
The definition of a user for this thesis is focused on defining an application ratio or
application ratio distribution that can be used as an input to the simulation model.
Having the raw data on the types of applications that are employed by mobile computer
users was the first step. In order to be utilized in the existing modeling framework, the
data needed to be distilled down to a single statistical distribution, while maintaining an
accurate representation of the of the variety of user profiles. Several methods of defining
the usage distribution were considered, both from the standpoint of how to characterize
each software type and how to characterize each user.
34
5.2.1 Software Characterization - Application Ratio
As discussed in Chapter 2, the dynamic power that a CPU consumes running a particular
software application varies significantly over time as it performs different operations. It
is useful to think of software's dynamic power consumption in terms of an application
ratio (AR).
Software applications, many of which are industry standard benchmarks that
corresponded to the 14 software types listed in the survey, were tested for their power
consumption. Two measures of power consumption were considered: average power and
the peak 5-second rolling average power. The power measurements for each software
type were converted to application ratios. The results are shown in Table 5.1.
Table 5.1: Software application ratios. Source: Intel Mobile Power Lab
Software Type
Word processing
Spreadsheets
Presentation Creation
Email
Internet browsing
Games
Audio/MP3
VideoNiewing DVDs
Video/Downloading from Internet
Video/inputting to Devices
Graphics/CAD
Web content creation
Programming tools
Simulations/math models
Average App
Ratio
0.29
0.29
0.29
0.18
0.22
0.71
0.32
0.27
0.47
0.86
0.59
0.78
0.77
0.69
35
Veak 5-Sec Ave App
Ratio
0.78
0.78
0.78
0.18
0.22
0.77
0.37
0.39
0.83
0.88
0.74
0.79
0.77
0.70
5.2.2 User Characterization
With information about the types of software applications that each respondent uses the
next challenge is to define a way to characterize each user. Two approaches are taken.
The first approach followed in the tradition of the worst-case methodology; each user is
defined by the most strenuous software type that he/she reported in the survey. A single
application ratio can then be associated with each user based on the application ratio
definition.
The second approach involved calculating the expected value of the application ratio for
each user. This is the average value of the application ratios reported used, weighted by
the percent time reported for each software type.
The expected application ratio calculated for each surveyed user is given by Equation 5.1:
E(AR)i= Al*Ti,1 + A2*Ti,2 +
... A14*Ti,14
where Al is Application Ratio for software type I
where Ti, 1 is % time user i reports for software type 1
Both methods produce different results depending on the definition of software
application ratio.
36
(Eq. 5.1)
5.3 Usage Model Matrix
A two by two matrix can be formed with the two definitions of software application ratio
and user characterization, as shown in Figure 5.1.
Software Application Ratio
Peak 5-Sec Average
0
N
Worst
Most conservative
Case
Will it ever happen?
Average
(-
0)
Least Conservative
-C
Expected
o
Will it happen most of the
time?
Figure 5.1: Usage Model Matrix
The nomenclature to describe each quadrant is the user characterization (Worst-Case,
Expected) followed by the software application ratio (Peak 5-Second Average, Average).
Each quadrant of the matrix encompasses a different degree of conservatism, and
represents a different time scale or frequency of events when used in the simulator. Each
of the quadrants is explored in detail, starting in the upper left and going counterclockwise.
5.3.1 Worst-Case Peak 5-Second Average Usage Model
The application ratio distribution that results from the worst-case 5-second average usage
model is displayed in Figure 5.2.
37
Worst Case 5-second
0.8
0.7
C
0.6
<0.5
0.4
0.3
9L 0.2
m
0 .1......
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Application Ratio
Figure 5.2: Worst-case 5-second average usage model application ratio distribution.
This application ratio distribution is very similar to the single value worst-case
assumption. This is because almost all users will at some point use software types that
have the potential to consume a high fraction of the processor power. This analysis
validates the choice of TDP application ratio for a quality analysis at worst-case
conditions.
However, this type of distribution offers no insight to how often a recurring event, such
as throttling, would occur. This type of input to the quality simulator will only answer
one time-based question - "Will it ever happen?" This type of analysis is appropriate for
catastrophic unrecoverable events, and may be appropriate for catastrophic recoverable
events (e.g. Fmax). This input is also useful to calculate the absolute maximum
temperature a CPU will see in use. However , it provides absolutely no information on
38
the frequency of events; would the event happen once a year? once day? once and hour,
once a minute? No conclusions can be drawn.
Does it make sense to use this as the standard for events where the consequences are
transitory and functionality is maintained? For example, a simulated person who used
their mobile computer for email, Internet browsing, and word processing would be
assigned an application ratio of 0.78 under this model. If that 0.78 application ratio, in
combination with the CPU, system, and ambient factors, created a throttling event, then
the result of the simulation would record the user as suffering from a throttling system.
However, if that user was word processing only 10 percent of the time, and word
processing software reaches it peak power infrequently and for short durations, throttling
may be imperceptible, if it occurs at all. In such a case, the user would never experience
a quality event. For these reasons the worst-case 5-second average usage model is not
useful for calculating throttling or other transient events, but it does provide an upper
bound on the number of units that could be affected.
5.3.2 Expected Peak 5-Second Average Usage Model
The application ratio distribution that results from the expected 5-second average usage
model is displayed in Figure 5.3.
39
Expected 5-Second Average
0.1
.!n 0.08
0.06
0.04
2 0.02
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Application Ratio
Figure 5.3: Expected 5-second average usage model application ratio distribution.
This is an interesting distribution that uses the peak 5-second average application ratio
definition, accounting for the highest powers (and hence temperatures) that each software
type can generate. The peak 5-second average application ratio definition also removes
any influence of processor idle time in the calculation of the application. At the same
time by using the expected user characterization, "credit" is given for time spent in low
power applications.
Running the simulation with this usage model input should produce a typical distribution
of maximum temperatures. Note that this is different than the absolute maximum
temperatures, which are calculated using the worst-case 5-second average usage model.
The expected 5-second average usage model also provides some insight into the
frequency of events. Basically, simulating with this type of usage model answers the
40
question, "Is it expected to ever happen?" This is actually quite different than "Will it
ever happen?" A processor could spend minutes throttling over its 1000's of hours of
operating lifetime. Did the processor ever throttle? Yes. Would the processor be
expected to throttle? No.
Let's return to the example person who used his/her mobile computer for email, Internet
browsing, and word processing. If this person spent 10 percent of his/her time word
processing and split the remaining time equally between email and Internet browsing
their expected 5-second average application ratio (from EQ 5.1 and Table 5.1) would be:
E(AR) = 0.78*0.10 + 0.18*0.45 + 0.22*0.45 = 0.258
With a 0.258 application ratio it is highly unlikely that the simulator would calculate a
throttling event. The interpretation of this result is that the user would not be expected to
be affected by throttling. This does not mean the user would never experience throttling.
For a different user with a higher expected 5-second average application ratio the
simulator may predict throttling. The interpretation of a throttling result is that the CPU
would be throttling "sometimes", not all of the time, but more than infrequently.
5.3.3 Expected Average Usage Model
The application ratio distribution that results from the expected average usage model is
displayed in Figure 5.4.
41
Expected Average
0.2
0.18
0.16
0.14 0.12
-
0.1
0.08
o 0.06
0 0.04
0.02
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Application Ratio
Figure 5.4 Expected average usage model application ratio distribution.
This distribution includes the effect of processor idle time when running applications, and
includes the influence of time spent in low power applications. Inputting this usage
model produces the expected average temperature distribution, but no information about
peak transient temperatures.
Using this distribution will only predict events that happen most of the time. This may be
useful for predicting the number of laptops that would never work properly due to
excessive rate of throttling. If a simulated CPU is predicted to throttle under
this model,
the interpretation is that on average it would be throttling to some degree for its active
life. Because this model will predict the long-term average temperature distribution, it
may be useful in long-term reliability modeling.
42
5.3.4 Worst-Case Average Usage Model
The application ratio distribution that results from the worst-case average usage model is
displayed in Figure 5.5.
Worst Case Average
0.4
0.35
.I
0.3
0.25
0.2
0.15
0
L
0.1
0.05
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Application Ratio
Figure 5.5: Expected average usage model application ratio distribution.
This model only accounts for the most strenuous application that each respondent
reported using in the survey. It then defines each of those applications by their average
application ratio, including processor idle and low-power time. Using this model will
predict the number of users that experience events on average when running their worstcase applications. However, it does not provide any information on how often these
events will happen because it does not include any information on how often the worstcase applications are used.
43
5.4 Application Ratio Distribution Parameters
To utilize an application ratio distribution model in the simulation program it is necessary
to define it terms of statistical distribution parameters. The first step was to choose a
usage model from the matrix. The expected 5-second average application ratio usage
model is chosen because it provides the best balance between the maximum power that
each software type can consume and the percentage of time spent in both high and low
power applications. It is a good compromise for throttling simulations between the
highly conservative worst-case 5-second average model, which will count throttling
events that happen so infrequently that a user may never notice, and the expected average
model, which only counts events if they are occurring more than half of the time.
Because the application ratio (AR) is bounded between zero and one, the data is
transformed into the Z variable as shown in Equation. 5.2.
Zi
=
log [E(AR)i/(1 -E(AR)i)]
The Z variable is then approximated by a normal, and the mean
(/A)
(Eq. 5.2)
and standard
deviation (&) are calculated.
The normal parameters are also adjusted by different weighting factors for each user (w)
from the survey data. The equations for the mean (fl) and standard deviation (&) are
given in Equation 5.3 and 5.4 respectively.
44
$=1
xZ2(w x Z,)
~
w^
x~
3=~
5.3)
X(Eq.
Wi
Wi
'X
Zi
z
)2
PEq.
(I1 5.4)
Multiple weighting factors and combinations are examined. The sample population
weighting factors (as described in 5.1), the hours of actual use reported by each user, and
the combination of the two are each used to calculate a set of normal distribution
parameters. The Z normal parameters are entered directly into the simulation program,
which transforms the Z values back into AR as shown in Equation 5.5.
AR=1Oz
/1Oz +1)
(Eq 5.5)
The four resulting application ratio distributions (raw, weighted by population factor, by
hours, and by population factor*hours) are shown in Figure 5.6.
45
Expected 5-Second Average
rpp
Ratio
j
'-- raw fit
-weighted
0
0.2
0.4
0.6
0.8
by Hours
-
weighted by
Population Factors
-
weighted by
Population
Factors*HOURS
1
Application Ratio
Figure 5.6: Expected 5-second average application ratio data and normal functions.
Weighting the data by the hours of active use that each user reported makes the
distribution more accurately reflect probability that a CPU would be operating at one
condition compared with another. Take the example of two users; one operates his/her
computer for 10 hours per week at an AR of 0.35; the second operates his/her computer
for 40 hours at an AR of 0.70. If we were to randomly sample the operating condition of
a CPU in this example, 0.70 would be four times more likely. Weighting the raw data by
the hours of active use corrects for this affect. It is interesting to note that weighting the
data by hours reduces the standard deviation. This suggests that people who spend more
time actively using their mobile computer also tend to have an average expected 5-second
average application ratio that is closer to the mean of the distribution.
46
As discussed in Section 5.1, weighting the data by the sample population factors accounts
for difference in the ratio of sample size to total population for different subgroups. It is
interesting to note that weighing the data by these factors does not significantly shift the
mean or standard deviation. This suggests that mobile computer users have similar
software type usage patterns across the geographies and sub-groups surveyed.
The result of the above analysis is that both the hours of active use and the sample
population factors are used in developing the normal parameters to be used in the
simulation model.
5.5 Ambient Temperature Distributions
A model of the mobile computer ambient temperature distribution is also developed from
the Mobile Choice Survey data. The base for this mobile ambient temperature model is
desktop model that was developed by Corporate Marketing Research. The desktop
model focuses on indoor ambient conditions, and concludes conservatively that 40
percent of desktop computers are used in an air-conditioned environment. The ambient
temperature estimations for non air-conditioned environments are based on daily
maximum summer temperatures.
Analysis of the mobile survey data shows that 87 percent of the active use time fell into
one of the four indoor categories: Office, Home Office, Home, Hotel. All of these
47
environments conservatively fit into the desktop indoor pattern described above. The
remaining 13 percent of the active use time (Airport, Car, Dorm, Classroom, Outdoors,
Other) was placed entirely in the non air-conditioned category. The resulting bi-modal
ambient temperature distribution for a mobile computer is shown in Figure 5.7.
-0.08
-0.06
-0.04
0.02
18 20 22 24 26 28 30 32 34 36
Ambient Temperature (deg C)
Figure 5.7: Mobile computer ambient temperature distribution model.
This chapter details the development of mobile computer usage models from end-user
survey data and software application ratios. A two by two matrix of usage models is
developed with different levels of conservatism in each quadrant. A model for the
mobile ambient operating temperature is also developed. In the next chapter the usage
and ambient temperature models are used in product quality simulations to predict
throttling events.
48
Chapter 6 - Simulation Results
A Monte Carlo simulation program is used to evaluate the expected operating
characteristics of microprocessors in mobile computer systems. Simulation results from
both worst-case operating conditions and a statistical usage model are analyzed and
compared. The statistical usage model is then used to compare the theoretical operation
and performance of a true mobile system and a desktop arbitrage transportable system.
Finally, an analysis is performed to evaluate the effect of reducing the throttle set point
on a mobile CPU, and how this could reduce the number of quality events in the field.
One hundred thousand simulations are used for all cases to tabulate results.
6.1 Worst-Case Operating Conditions vs. Statistical Usage
Model
The first case to be investigated was a simulation of a 2.2 GHz mobile CPU in a
theoretical traditional thin & light mobile system with a the thermal solution capability
that is designed to Intel's recommendations for that CPU. A comparison between the
results using the TDP application ratio user model and the expected 5-second average
application ratio user model and mobile ambient temperature distribution was performed.
As shown in Figure 6.1 a and 6. 1b, the average predicted CPU temperature (Tj) drops by
over 20 degrees C when evaluated with the expected 5-second average application ratio.
49
The range of Tj also increases dramatically, by more than a factor of four, and the
distribution is better described by a normal distribution as predicted by the Central Limit
Theorem. This is the expected result of convolving an additional normal distribution in
the calculation in place of a single worst-case value.
-
-4.
-4
- 3
-3.
044
E
E
a
0
0
Z
0
---
ns-
2
ni-3
F.ue
.n
P
Mli
tepeatr
-:1rditd
n
s
o
.
~
50
60
70
80
Tj in Use (deg C)
90
100
40
50
60
70
80
Tj in Use (deg C)
90
100
(a)
(b)
Figure 6.1la & b: Predicted CPU temperature distribution (Tj) in use for a 2.2 GHz
mobile CPU in a thin & light system using (a) Worst-case operating conditions (b)
Statistical usage model.
The impact throttling, power reduction, and performance of this significant shift in
predicted CPU temperature is summarized in Table 6. 1.
50
-2
-3
-1tiuin(j
Pu
sz
40
Z
Table 6.1: Predicted operating characteristics of 2.2 GHz mobile CPU thin & light
systems.
Worst-case operating
conditions
CPUs throttling
Statistical usage
model
0.91%
0.01%
9.1%
9.7%
5.1%
16.4%
(90% upper confidence limit)
Maximum power reduction
required
Maximum performance
reduction
The 90 percent upper confidence limit on the percentage of CPU's that will experience
throttling in the field drops by almost two orders of magnitude.
It is interesting to note that to maintain the CPU temperature limit, the maximum power
reduction required is slightly greater when using the expected 5-second average
application ratio distribution in the simulation. This is true because, with the application
ratio distribution, it is possible to simulate an application ratio higher than the TDP
application ratio. The higher application ratio results in higher power consumption,
which then requires a greater power reduction to maintain the temperature limit under
certain conditions. Also of interest is that the slightly greater maximum power reduction
required in the application ratio distribution simulation. This results in more than three
times the performance loss. This is due to the highly non-linear relationship between
power reduction and performance loss.
It is possible to examine in greater detail the impact to the CPU's that were predicted to
throttle. Figure 6.2 shows the fraction of the power reduced to maintain the CPU
temperature limit with the TDP application ratio for the subset of the CPU's that are
51
throttled in simulation. It is important to remember that the throttling subset represents
only 0.91% of the total population.
0.9-
-0.20
I0.8-
0.15
EL 0.5
n
-0.1 a0
0
0.3-
CL
-0.050.
0.10
.01 02 03 .04 .05 .06 .07 .08 .09
0
Power Reduction
.01
.02 .03 .04 .05 .06 .07 .08 .09
Power Reduction
(a)
(b)
Figure 6.2a & b: Predicted power reductions for throttling 2.2 GHz mobile CPU in thin
& light systems using worst-case operating conditions (a) Histogram (b) Cumulative
distribution function.
As can be seen from Figure 6.2b, half of the CPU's that are simulated to throttle under
the TDP usage model require less than two percent power reduction to maintain the
temperature limit. Figures 6.3a and b show the CPU performance reduction as a result of
the power reductions of Figure 6.2a and b
52
.
-0.50
00.9-
-0.40
0.0.7-
0.30
8.
>,
00
0.2020..1-
.01
.4.04
0
.02 kirirt~
.03
Performance Impact
.50
.05
.01
.02
.33.04
.05
PromneIpc
(a)
(b)
Figure 6.3a & b: Predicted performance reductions for throttling 2.2 GHz mobile CPU
in thin & light systems using worst-case operating conditions (a) Histogram (b)
Cumulative distribution function.
Due to the inexact nature of the performance correlation there is a slight offset at zero.
What is important to note is that the majority of the CPU's that are predicted to throttle
under the TDP application ratio assumption experience less than one percent performance
impact. In fact, more than 90 percent of the throttling CPU's would suffer from less than
two percent performance impact.
This validates Intel's current mobile thermal design process. Intel starts with the
assumption of the TDP application ratio user model and a 35 degree centigrade ambient
temperature. Its success criteria are that throttling will only occur in rare circumstances
and the performance impact should be practically undetectable. The simulation validates
that using those assumptions, Intel meet its goals.
53
The picture looks very different with the expected 5-second application ratio distribution.
Only one unit out of the 100,000 simulated with the application distribution indicated
throttling. This was only in the extremely rare instance of a high ambient temperature
(Ta > 33C), an extremely high application ration (AR > 0.85), and a high power CPU.
6.2 Mobile System vs. Desktop Arbitrage System
Simulations were run to compare the performance and occurrences of throttling between
two theoretical systems; a thin & light system with a 2.2 GHz mobile processor as in
Section 6.1, and a transportable system with a 3.06 GHz desktop processor. The desktop
processor can consume up to 146 percent more power than the mobile processor, and that
power needs to be dissipated. The larger physical size of the transportable system allows
for a thermal solution with a greater cooling capacity. For these simulations the thermal
solution capability of the transportable system is 72 percent greater than the thin & light
system, which is typical. However the transportable system with the desktop processor is
still 31 percent thermally under-designed per Intel's current design recommendations.
The OEMs may design and build thermally under-designed systems because they realize
Intel is using a worst-case design methodology, and the OEMs may believe they can
relax the thermal design conditions (i.e. the ambient temperature and processor power)
without suffering any consequences.
The first result of the simulation that needs to be mentioned is that with the TDP
application ratio user model and a 35 degree C ambient temperature, Intel's current
design assumptions, the simulation predicts that all of the desktop processor transportable
54
systems will experience severe throttling. They would all require a 10% - 50% power
reduction to maintain the CPU temperature limit. If such extreme conditions are realistic
it is safe to say that many of the systems would fail to operate at all.
Running the simulation with the expected 5-second average application ratio and the
ambient temperature distribution use model produces results that are more indicative of
real world use.
As shown in Figure 6.4a and 6.4b the average predicted CPU temperature (Tj) is
approximately 15 degrees C higher for the "transportable" desktop CPU system when
compared to the "thin & light" mobile CPU system.
55
.........
.....
......
.....................................
..
-...........
-........
.......
...........
...
.....................
.......
..................................
.........
.. .................
.............
....
- 1..
...........
-4
4
1
-3
-3 A?
-.2
-2
M1
Hi
La
.10
--1
-- 1
-- 2
13
7
--3
Lii
Dl]
-0.04
80
60
70
Tj in Use (deg C)
-- 3
-0.13
A
-0.10
-0.08
-0.06
50
-- 2
II
0.08
40
Z
a0
90
n
-0.05
-0.02 -
-0.03
40
100
50
60
70
80
Tj in Use (deg C)
90
a..
100
(b)
(a)
Figure 6.4a & b: Predicted CPU temperature distribution (Tj) in use for (a) 2.2 GHz
mobile CPU in a thin & light system using (b) 3.06 GHz desktop CPU in a transportable
system
The effect of the thermal monitor can clearly be seen in Figure 6.4b. The thermal
monitor limits the right hand side of the temperature distribution to approximately 100
degrees C.
The impacts of these predicted CPU temperature distributions are summarized in Table
6.2.
56
Table 6.2: Operating characteristics predicted with statistical usage model of theoretical
mobile and transportable computer systems
CPUs throttling
(90% upper confidence limit)
Maximum power reduction
required
Maximum performance
reduction
2.2 GHz mobile CPU
thin & light system
0.01%
3.06GHz desktop CPU
transportable system
19.6%
9.7%
49.1%
16.4%
Some systems would not
operate properly
The 90 percent upper confidence limit shows that nearly 20 percent of the desktop
processor transportable systems would be expected to experience some degree of
throttling. This also means that 80 percent of the systems, if equally distributed among
standard mobile computer users, would not be expected to experience any significant
throttling. However transportable system users may have a different profile than standard
mobile computer users.
The distributed usage model still predicts that some of the desktop processor
transportable systems are thermally constrained to the point that not only the processor
performance suffers, but also they may fail to operate entirely.
We can investigate in more detail the impact to the 19.6 percent of the desktop CPU's
that are predicted to throttle. Figures 6.5a & b show the fraction of power reduction
required to maintain the temperature limit for the subset of the CPU's that throttle.
57
0.0
0.90.8-
-0.06
0.04
_0.7-
CC
-0.02
E
050.40.30.2-
0.1-
0
.1
.2
.3
Power Reduction
.4
0
.5
.1
.2
.3
Power Reduction
.4
(a)
(b)
Figure 6.5a & b: Power reduction predictions under the statistical usage model for
throttling 3.06 GHz desktop CPU in transportable systems (a) Histogram (b) Cumulative
distribution function.
From Figure 6.5b, 50 percent of the throttling desktop CPU's require 10% power
reduction or less. These systems would continue to function, although at varying degrees
of full performance when compared with an un-throttled equivalent system. Analysis of
the performance of the entire throttling sub-population is shown in Figures 6.6a & b.
-0.70
0.9-
-0.60
0.7-
-0.50
-00.60.5E
0.40
-0.30
S0.4 -
.
0.20
0.2 -
-0.10
0
.1
.2
.3
Performance Reduction
0.1-
.4
Performance Reduction
(b)
(a)
Figure 6.6a & b: Performance reduction predictions under the statistical usage model for
throttling 3.06 GHz desktop CPU in transportable systems (a) Histogram (b) Cumulative
distribution function.
58
Fifty percent of the throttling desktop units, which represent approximately 10 percent of
the total population, suffer less than a five percent performance reduction. However
beyond that the performance of the remaining sub population falls off dramatically. This
is due to the highly non-linear nature of performance loss.
The performance loss correlation is capped at 40%, which results in non-uniform
distribution for highly thermally stressed systems, such as in Figure 6.6a & b. The reality
is that at high levels of thermal stress, throttling and power reduction (>>10%), the
performance of the processor is no longer the critical issue, rather function of the system
becomes the critical issue. There are documented cases [12] of desktop processors used
in mobile systems that resulted not only in reduced performance, but lack of function.
With this data we can now make a direct comparison between the performance of a 2.2
GHz Mobile CPU thin& light system, and a 3.06 GHz desktop CPU transportable. For
the mobile systems 99.99 percent would operate at their full rated performance. Only
under very rare and extreme use conditions would they experience any significant
throttling. In comparison, 80 percent of the desktop transportable systems would perform
at their full performance level. However, the full performance of the desktop
transportable system is greater than the mobile system's performance due to the desktop
CPU's higher speed rating.
59
6.3 Recommended Change to Throttle Set-Point
Another interesting analysis that can be performed with this methodology is the impact of
the throttle temperature on the number of quality events that will be experienced in the
field. Returning to the theoretical 2.2 GHz mobile CPU thin & light systems, multiple
simulations were run where the only variable changed was the temperature at which the
CPU began to throttle. The system parameters were held constant to reflect no change in
the thermal solution. By definition the simulated systems with the lower throttle set point
are thermally under-designed because there is less of a temperature difference to drive the
heat out of the system (see Eq 3.3).
There are two types of events that are of concern in this analysis, frequency maximum
(Fmax) events, and throttling events. Fmax events occur when, due to elevated
temperature, the processor cannot run at its prescribed frequency. Fmax events increase
with increasing CPU temperature. Throttling events occur when a CPU reaches the
throttle set point temperature, and a power reduction is required to not exceed that
temperature. For a given CPU temperature distribution reducing the throttle set point
increases the number of throttling events.
Figure 6.7 shows the simulation results of how the two events vary as a function of the
throttle temperature set point when evaluated with the standard worst-case TDP
application ratio and 35 'C ambient temperature.
60
--U--
o
88
90
92
Fmax
Throttle
Total
94
96
98
100
102
Throttle Set Point
Figure 6.7: Predicted quality events as a function throttle set point for a 2.2 GHz mobile
CPU thin & light system under worst-case operating conditions.
As can be seen in Figure 6.7, in this worst-case scenario throttling events dominate the
Fmax events. To minimize the total number of quality events the throttle set point should
be set at 100 (set points above 100 were not considered).
Using a statistical usage model to provide a more realistic view of the number of
throttling events provides a very different picture. Figure 6.8 represents the simulation
results when the expected five-second average application ratio distribution and ambient
temperature distribution are used to calculate the CPU temperatures and hence the
occurrences of throttling.
61
0
--
0
Fmax
Throttle
. Total
8.
3
90
92
94
96
98
100
102
Throttle Set Point
Figure 6.8: Predicted quality events as a function throttle set point for a 2.2 GHz mobile
CPU thin & light system under statistical usage model.
As shown in figure 6.8, the number of predicted throttling events no longer dominates the
Fmax events when evaluated with the distributed usage model. With the throttle set point
at 100, the expected number of throttling events is five times less than predicted with the
worst-case use model. And, as the throttle set point is reduced from 100 the number of
Fmax events declines more rapidly than the number of throttling events increases.
Therefore to minimize the total number of quality events the throttle set point should be
reduced to 95. This will reduce the total number of quality events by 37 percent and the
number of catastrophic events by 57 percent.
This is counter-intuitive to the traditional thermal design mindset. Recall that no changes
are made to the definition of the thermal solution for this theoretical system. The thermal
62
solution is appropriately sized for a CPU with a maximum temperature of 100. As the
CPU temperature limit is reduced, thermal design principals state that a larger thermal
solution is required. Otherwise the system will be considered thermally under-designed.
We know that thermally under-designed systems generate a greater number of quality
events for a given throttle set point. However, by adjusting the throttle set point a tradeoff can be made between Fmax and throttling quality events. As shown in Figure 6.8, the
number of events can go down when system is thermally under-designed by virtue of a
lower throttle set point. Of course, lowering the throttle set point and specifying a larger
thermal solution could achieve even lower event rates. That would place additional
burden on system designers and OEM's, and runs counter to the trend of making systems
smaller and lighter.
Perhaps the most important aspect of these results is that the thermal design process need
no longer be one-dimensional. The size and capacity of the thermal solution is not the
only variable Intel can manipulate in its thermal design and specification process. By
using the throttle set point, the thermal solution size, a realistic representation and
understanding of throttling and Fmax events, and an understanding of the OEM thermal
solution capabilities, Intel can better optimize the system.
It is also important to understand the impact on the CPUs of the proposed change to a
95*C throttle set point. Figures 6.9 and 6.10 show detailed information on the simulated
CPU temperature distributions for a 2.2 GHz mobile CPU thin & light system with a
throttle set point of 100*C and 95*C, respectively.
63
'4
2
-3
-2
S
C
a
.75-
Quantiles
15
--
z01
25-
-- 2
100.0% maximum
99.5%
97.5%
90.0%
75.0%
quartile
50.0%
median
quartile
.25.0%
10.0%
2.5%
0.5%
0.0%
minimum
I
98.723
92.469
88.130
82.639
77.197
70.920
Moments
Mean
Std Dev
Std Err Mean
upper 95% Mean
lawer 95% Mean
N
70.83124
9.0753978
0.0514445
70.932074
70.730407
b4.4U
58.973
53.011
48.213
38.949
ni
-0.058
-0.04
-0.042
40
50
60
70
80
Tj in Use (deg C)
90
100
Figure 6.9: Predicted CPU temperature distribution (Tj) in use for a 2.2 GHz mobile
CPU with throttle set point at 100*C in a thin & light system
64
31121
-2
m- 1~
.
:3
as
G
0
.r-1
.1a0
"5DI-
--213
--
m.- -32
i
L --
Moments
Quantiles
-3
-3
100.0% maximum
99.5%
97.5%
90.0%
75.0%
quartile
50.0%
median
25.0%
quartile
10.0%
2.5%
0.5%
0.0%
minimum
97.162
92.418
88.130
82.639
77.197
70.920
64.548
58.973
53.011
48.213
38.949
Mean
Std Dev
Std Err Mean
upper 95% Mean
lower 95% Mean
N
-I70.82B178
9.0673384
0.0513988
70.928921
70.727434
31121
1.------
-2500
2000-.!
-1500
40
40
50
60
70
80
U
90
Figure 6.10: Predicted CPU temperature distribution (Tj) in use for a 2.2 GHz mobile
CPU with throttle set point at 1 00*C in a thin & light system
As can be seen in a comparison of Figures 6.9 and 6.10, reducing the throttle set point
from 100*C to 95*C has a negligible impact on the overall temperature distribution. This
is expected as the throttle set point only affects the far right tail of the distribution.
Despite the minor effect on the overall temperature distribution there is a noteworthy
effect on the number of units that throttle, and the performance impact of the throttling.
A comparison of the summary data is presented in Table 6.3.
65
Table 6.3: Operating characteristics predicted with statistical usage model of 2.2 GHz
mobile CPU thin & light systems.
Throttle Set Point
100 0C
CPUs throttling
(90% upper confidence limit)
Maximum power reduction
required
Maximum performance
reduction
Throttle Set Point
95 0 C
0.01%
0.15%
9.7%
19.9%
16.4%
43.4%
The 90 percent upper confidence limit of the number of units throttling has increased by
a factor of 15, but it is still less than 1 in 650. The maximum power reduction and the
associated performance loss to keep all of the CPU temperatures within specification
have also more than doubled.
Figures 6.11 a & b show the distribution of the power reduction required to keep the
throttling CPUs at the 95*C temperature limit.
66
1.0-
0.9-
-0.20
0.80.7 -
- 0.7
-0.15
~0.5D.E5 -0.10
_
a0.3-0.05
0.20.1
0 .02 .04 .06 .08 .1 .12 .14 .16 .18 .2
Power Reduction
0
.05
(a)
.1
Power Reduction
.15
(b)
Figure 6.11 a &b: Predicted power reductions for throttling 2.2 GHz mobile CPU with
throttle set point at 95*C in thin & light systems using statistical usage model (a)
Histogram (b) Cumulative distribution function.
Fifty percent of the CPU's that throttle need less than a five percent power reduction, and
90+ percent of the CPU's that throttle need less than 10 percent power reduction to stay
within the maximum temperature specification. Again, the one outlier at 20 percent
power reduction is the result of the extremely rare instance of a high ambient temperature
(Ta > 33C), an extremely high application ratio (AR > 0.85), and a high power CPU.
Figures 6.12a & b show the results of translating the power reductions into performance
reduction metrics. Over 90 percent of the throttling CPU's would suffer less than five
percent performance loss. The remaining few percent of the throttling population may
suffer significant performance loss, or even experience some functional problems,
however these would be at most 1 in 100,000 of the total population.
67
.2
1.0-1.00
0.90.8-
0.75
0.7-
-
no 0.6
-0.50B
~0.4
0.30.2
0.1
0.25
0
.1
.2
.3
Performance Reduction
r-
0
.4
I
I
.05
.1
(a)
I
I
I
I
I
I
.15 .2 .25 .3 .35
Performance Reduction
.4
(D)
Figure 6.12a &b: Predicted performance reductions for throttling 2.2 GHz mobile CPU
with throttle set point at 95*C in thin & light systems using statistical usage model (a)
Histogram (b) Cumulative distribution function.
This chapter reviews the results of the three simulation comparisons: worst-case
operating conditions vs. statistical usage model, mobile system vs. desktop arbitrage
system, and 100 *C vs. 95 *C throttle set point. The next chapter concludes with
recommendations and future uses for the methodology developed.
68
~1
.45
Chapter 7 - Recommendations and Future Use
The results of this work point towards three topics for future consideration. The first
focuses on validating the process. The second involves new ways of segmenting the
market and opportunities for new products. The third focuses on strategic relationships
within the industry.
7.1 Validation
One of the barriers to adopting this usage model based methodology is that some people,
engineers in particular, do not believe that users can accurately report on a survey the
percent time that they use each software type. Admittedly the data is not precise as most
respondents parsed their time down to five or ten percent increments, and the accuracy of
any one user is questionable. However in the opinion of the author the data accurately
represents the mobile user population as a whole. We could not determine any reason that
there would be systematic bias in the data.
To overcome this barrier a validation of the survey results is desirable. One method is to
place software on the laptops of a representative group of mobile users that records the
applications being used. The software can periodically sample and record the application
data from the Windows Task Manager shown in Figure 7.1.
69
Figure 7.1: Windows Task Manager showing applications being used.
The aggregate data sampled from the Windows Task Manager would then be compared
to the aggregate data from the end user survey. The distribution of software use should be
similar. The group of users having their Windows Task Manager sampled could also be
given a use survey to fill out. A direct comparison of reported use pattern to measured
use pattern on a per user basis could be performed. In many ways this validation
approach is the simplest because it is independent of the computer system hardware. It
focuses only on the software applications.
70
It would be tempting to sample the CPU Usage function from the Windows Task
Manager and use that as a proxy for the software application ratio. This would skip the
step of measuring and assigning an application ratio to each software type. Unfortunately
the CPU Usage in the Windows Task Manager does not correlate well with the power
consumed by the CPU [8].
The most important output of this methodology and simulation is the CPU temperature
distribution profile. This is the variable that drives throttling and performance impact.
Ideally one would like to validate the temperature distribution output from the simulation
to real-world data. This is possible, software can be placed on a computer that samples
and records the CPU temperature. However to make a valid comparison one would need
a appropriately sized representative group of mobile users, all of whom had the same
computer system and processor. The logistics of coordinating such a study is daunting.
7.2 Market Segments and Product Positioning
Section 6.2 shows that 3.06 GHz desktop processor in a transportable form factor may
not perform to its full potential for all users. In fact, because they are thermally underdesigned and cannot properly cool the microprocessor, as many as 10 percent of those
systems may fail to function properly for their end-users. However there is significant
demand for higher power processors in the transportable segment as evidenced by the
increase in desktop arbitrage. Using the simulation and methodology presented here as a
path-finding tool, Intel could determine how powerful a processor can operate adequately
71
in a transportable form factor. They could then increase its mobile product offerings to
include processors up to that power level that also have other mobile specific features
enabled.
Section 8.3 shows that reducing the throttle set point to 95 0 C on a traditional mobile
processor can reduce the number of total quality events that the general population
experiences by 37 percent and the catastrophic events by 57 percent. However this
would leave a small group, approximately 0.1 percent of the population, of high power
users who would be stressing their processor to the point that they would suffer
significant performance loss.
One solution is to allow those users to identify themselves as high power users and offer
them a processor and/or system that meets their needs for a premium price. From a
technical perspective there are several ways to satisfy their demands. Intel could select
processors from the screening at the end of the production line that can operate reliably at
higher temperatures, set the throttle set point on those processors at 100 0 C, and sell them
into the premium segment. Or Intel could select the processors that have the lowest
leakage current, and therefore run at the lowest power and lower temperatures, and sell
then into the premium segment. A third option is that Intel could enter into closer
partnerships with one or more of the OEM's and have them offer a premium system with
a more robust thermal solution for the power user segment. The importance of these
partnerships is discussed in the next section.
72
7.3 Industry Dynamics and Strategic Partnerships
Much of Intel's success in the computer industry is attributed to the modular product and
supply chain architecture of the personal computer [13]. The relationship between
system thermal performance and CPU performance analyzed in this thesis points to a
product architecture that is becoming more integral. In particular, as processing power
increases and mobile systems become smaller, overall system design becomes more
important and needs to be better integrated, as predicted by Whitney [14]
This could be problematic for Intel in the future. Intel has built the "Intel Inside*" brand
on the premise that the processor is the most important feature of a PC, more so than even
the OEM brand. However if overall system design begins to be the constraint on
performance, consumers may place less emphasis on processor brand and more on the
OEM or system designer. This would be a significant shift in the power of the industry
players and a major loss for Intel.
Intel should use the resource of its brand strength to leverage themselves into other areas
of the mobile PC architecture, particularly in areas where integration is becoming more
important. A recent example is the launch of Intel@ CentrinoTM mobile technology [15].
The Intel@ CentrinoTM mobile technology is package consisting of a CPU, chipset, and
wireless network connection. We suggest that Intel also form closer partnerships with the
OEMs and system designers. As the CPU performance gains greater dependence on the
73
thermal system performance, it becomes in Intel's best interest to do all that it can to help
its customers design successful systems. Closer relationships with the OEMs would
allow Intel more input to the design and verification of thermal solutions, and perhaps the
next incarnation of "Intel Inside*" will include not only a CPU, chipset and network
connection, but a promise of thermal system and processor performance as well. This
may be crucial for Intel's survival if the double helix industry/ product structure, as
described by Fine in Clockspeed, continues to head towards more integration [13].
74
Appendix A: Selected Questions and Sample Answers
from End-User Mobile Choice Survey
@ 2002 Intel Corporation
8.
Thinking of a typical week, please allocate your notebook usage across each of the following
environments, so that the total equals 100%. Please enter '0' to any option not applicable to.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Office (not home office)
Office setting in home
Home (other than desk/office setting)
Airport
Car, train, bus, or airplane
Hotel
Dorm room
Classroom or library
Outdoors
Other location (please specify)
%
%
%
%
%
%
%
%
%
%
12. Approximately, how m any hours each week is your notebook computer turned on? Please provide
your best estimate and record whole hours only.
13. When it is turned on, approximately, how many hours each week do you run your notebook
computer from its batteries, rather than plugging it into a wall outlet? Please record whole hours
only.
14. Approximately, how many hours each week are you actively using your notebook computer?
Please provide your best estimate and record whole hours only.
21 .Of the time that you, personally, spend using your laptop in an average week, what percent of the
time do you spend on each of the following activities, for either business or personal use? Please
allocate your time spent on each activity so that the total adds up to 100%. Please enter '0' for any
activity that does not apply to you.
1.
2.
3.
4.
5.
6.
Word processing (e.g. Microsoft Word)
Spreadsheets (e.g. Excel)
Presentation creation (e.g. Powerpoint)
Email
Internet browsing (excluding email)
Games
%
%
%
%
%
%
7.
Audio/MP3
%
8. Video - Viewing DVD's
9. Video - Downloading video from the Internet
11. Video - Inputting video files to other devices, e.g.
camcorders or digital cameras
%
%
%
11. Graphics/CAD
%
12. Web content creation
13. Programming Tools
14. Simulations/Math Models
%
%
%
75
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