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A comparative assessment of the carbon footprint of AMD Fusion™ products with the previous generation products

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A comparative assessment of the carbon footprint of AMD Fusion™

products with the previous generation products By Siddharth Jain

I. Abstract

The AMD Fusion Family of Accelerated Processing Units (APUs), introduced to market in January 2011, is a new generation of processors that

combines the computing processing unit (CPU) and graphics processing unit (GPU) capabilities in a single chip (die). APU-based platforms can deliver a

prodigious amount of computational horsepower, and can present enormous opportunities in developing an application ecosystem beyond today’s

mainstream computer systems.

While APUs seek to deliver a superior, immersive PC experience, they also can provide tangible environmental benefits. By eliminating a chip to chip

link and by introducing new holistic power management techniques, the APUs are designed to be more power efficient than current generation platforms

that have both computational and graphical capabilities.

This paper compares the environmental impact of one of AMD’s first APU products against an equivalent computer platform powered by the current

generation of AMD processors (CPUs and GPUs). By conducting a business to consumer (B2C) lifecycle assessment, this study compares the total

lifecycle greenhouse gas (GHG) emissions (also known as a “carbon footprint”) of an APU system (based on the 18W dual-core processor codenamed

“Zacate” and the M1 chipset codenamed “Hudson”) with the latest AMD system codenamed “Nile” (which is based on an AMD Athlon™ Neo II Dual Core

processor, SB820 Southbridge, RS880M Northbridge with an ATI Mobility Radeon™ HD 5430 discrete graphics card). This study concludes that the

APU system offers significant GHG benefits (up to a 40% emissions reduction) when compared with the Nile platform.

II. Introduction

Climate change has become perhaps the most important environmental issue of our time. Policymakers, businesses, and consumers alike have made

this issue a central focus. In light of the environmental threat and the emerging GHG regulatory paradigm, businesses are increasingly taking steps to

improve their environmental performance as it relates to climate protection.

AMD has a long history of corporate responsibility and has repeatedly made, met, and in many cases, exceeded ambitious environmental goals. From

2001 through 2009, AMD published a report solely dedicated to its climate protection actions. In each report, AMD transparently and voluntarily tracked

progress to goals established to protect our climate.

With the recent changes at AMD to transfer its major manufacturing assets to a joint venture, the company’s focus on climate protection has also

changed. Without fabrication facilities under its operational control, AMD concentrated efforts in studying the effects of its products on the climate. One

example is a recently completed carbon footprint study of AMD’s Phenom™ II X4 processors (The Phenom Footprint Study) using the Carbon Trust

Footprint ExpertTM

tool (1).

Carbon footprints are one of the more widely accepted tools available to businesses to assess the environmental impact of their products, and the

results of these studies can be very instructive. For example, it is clear from this study and others referenced herein, that the largest carbon impacts of

semiconductor products come from the use of the product itself as opposed to the impacts from its manufacturing or other life cycle stages. While the

focus and resources applied to carbon emission reductions in the manufacturing stage are important, far more emissions reductions can be achieved by

focusing on the design and use of the product.

This paper compares two generations of AMD processors with very different designs. Through a life cycle carbon footprint assessment, we compare the

AMD E-350 APU (formerly codenamed “Zacate”) to the platform codenamed “Nile” that has roughly an equivalent performance. Because the APU has

both computational and graphics processing capabilities, this study compared an APU platform (with the “Hudson” chip) against an equivalent

performing platform comprising of a central processing unit (CPU), graphics processing unit (GPU) and other associated chips.

III. Objective

The overarching objective for this study is a quantitative comparison of the carbon footprint of an APU platform against a platform consisting of separate

CPU and GPU elements with roughly equivalent processor performance. This study focused more on a relative comparison rather than absolute carbon

emissions associated with a particular processor or platform. There was also a focus specifically on the product use phase. Findings from other studies

are used, when available, to provide data for other phases such as manufacturing and supply chain emissions, which have much smaller impacts on the

total carbon footprint. By focusing on the relative impacts of the two systems, the intent of this work is to reveal the environmental implications of the

transition to APU platforms.


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IV. Product Selection

A. AMD Fusion™ Technology and the Accelerated Processing Unit

AMD Fusion is a new approach to processor design, combining x86 CPU cores with the vector processing engines of GPUs on to a single chip (die).

Figure 1i and Figure 2, respectively, show the differences between APU architecture and current generation Nile architecture.

In the Nile platform (Figure 2), Northbridge (memory controller, Integrated Graphics) and the Southbridge (I/O controller hub) reside on three different

chips. In this architecture, power is required to maintain chip-to-chip linkages. With APUs (Figure 1), the CPU and the Northbridge have been combined

on one chip. Additionally, more graphical performance has been added to the

APU model.

B. Selection Criteria

In the selection of the products for comparative assessment, the key criterion is

the equivalency in performance of the systems. Commercial client PC

benchmarking is currently the most common way of gauging real world

performance of PCs (2). There are many existing benchmarks for comparing

processor performance, with the most widely recognized being PCMark Vantage,

MobilMark®, SYSMark®, and 3DMark. In selecting which benchmarks to use to

determine comparability, AMD places greater weight on the benchmarks that it

believes reflect the user experience more accurately. For example, the usage

models in the PC industry are changing, thanks in no small part to the explosion

in multimedia and digital content. This had led to greater importance being

placed on graphics performance rather than pure CPU horsepower.

With these criteria in mind, PCMark Vantage and 3DMark were selected as the

performance benchmarks for this study. PCMark Vantage measures the

computing performance across a variety of tasks such as office productivity,

music and media, viewing and editing photos etc. 3DMark on the other hand,

measures the processing performance for 3D graphics.

Two reference systems, with a value Notebook PC as the underlying system, were chosen for this carbon footprint comparison because their computational and graphics performance are closely matched based on the PCMark Vantage and 3DMark benchmarks (see Table 1).

1. The 18W APU reference system comprised of two chips: a. “Zacate” 18W APU dual core 1.6 GHz processor b. “Hudson” M1 (or the Southbridge) controller

2. The Nile reference system comprised of the following chips:

a. AMD Athlon™ Neo II Dual core b. RS880M 55nm (Northbridge) c. SB820 Southbridge d. ATI Mobility Radeon™ HD 5430 graphics processor

While the APU reference system has 77% of the computational performance of the Nile reference system based on the PCMark Vantage benchmark, it

has 93% of the graphics processing performance based on the 3DMark score. With the increasing use of the GPU for computational capabilities (3) and

the possible range of applications that can leverage the enormous GPU compute power (4), it makes sense to consider both benchmarks for a

comparison.

V. Semiconductor Life Cycle Assessment– an Introduction

A. Life cycle assessment

Life Cycle Assessment (LCA) is a quantitative analytical method used to evaluate the total environmental impact arising from production, use, and end of

life phases of a product or service (5). A carbon footprint on the other hand, is a subset of LCA methodology with analysis limited to emissions that have

an effect on climate change. A carbon footprint of a product refers to the total set of GHG emissions (CO2, CH4, N2O, PFCs, etc) associated with the

Performance

“Zacate” 18W APU system (“APU”

reference system)

“Nile” Platform with Park LP discrete graphics (“Nile”

reference system)

PCMark® Vantage 2300 (77%) 3003 (100%)

3DMark Vantage E (1024x768) 3384 (93%) 3652 (100%)

Table 1: Performance of the two reference systems

Figure 1: Schematic of the APU system (CPU, GPU on single die/chip)

Figure 2:: Schematic of Nile

system (CPU, Northbridge, and Southbridge on different chips


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product in its life cycle – i.e. raw materials, manufacturing, transportation, product use and disposal. Figure 3 shows an overview of various stages in

the life cycle of a semiconductor product.

According to the GHG Protocol developed by the World Resources Institute (WRI) and the World Business Council on Sustainable Development

(WBCSD) (6) these emissions are categorized as follows:

i. Scope 1 emissions – all direct GHG emissions from sources that are owned or controlled by the reporting company. For example, combustion of fuels are categorized as Scope 1 emissions.

ii. Scope 2 emissions – indirect emissions associated with the generation of imported/purchased electricity, heat, or steam.

iii. Scope 3 emissions – all of the other indirect emissions that are a consequence of the activities of the company, but which occur from sources not owned or controlled by the company such as commuting, waste disposal, production, and transportation of raw materials and final products etc.

While several methods are under development (e.g. ISO 14067 (7)),

there is no commonly accepted standard for a carbon footprint of a

product. PAS 2050:2008 (Specification for the assessment of the life

cycle greenhouse gas emissions of goods and services) is a publicly

available specification (not a standard) for carbon footprint of

products, which attempts to establish a consistent method for

assessing life cycle GHG emissions (8). This study attempts to

follow this specification closely although certain concessions had to

be made with prudent judgment due to data limitations.

A carbon footprint assessment begins with the definition of a

functional unit, a reference unit for which the GHG emissions are to

be measured (8). For this study, the functional unit was a single chip

for which the life cycle analysis was conducted. Final results are

reported for the reference systems which are a combination of these

chips (defined in the Product Selection section).

The next step is to establish the scope or boundary of the carbon footprint (business to business or business to consumer). The scope of this study is a

business to consumer footprint of the two reference systems. Within the boundaries of this study are raw materials, manufacturing (fabrication,

assembly, test, marking and packaging), and consumer use. The distribution/retail, end-of-life stages and manufacturing of process tools have been

excluded from the analysis. The next section discusses the stages within the boundary of this study in detail.

VI. Methodology

In early 2010, AMD completed the Phenom Footprint Studyii in collaboration with GLOBALFOUNDRIES and Carbon Trust, and certified by Carbon Trust

to be in accordance to PAS 2050. The Phenom Footprint Study provided valuable insights into the business to business (B2B) climate impacts of key

product life cycle stages such as supply chain and manufacturing. This study relied on the Phenom Footprint Study for emission estimates for some of

the manufacturing phases of the life cycle.

The boundaries for this study begin with the fabrication of the silicon to make the chips and end with the use of the chips in PCs. While there are other

aspects of the product life cycle before and after these boundaries, they were beyond the scope of this study. Therefore, stages like distribution and

retail, end-of-life (disposal, remanufacturing etc), certain select Scope 3 activities like employee commute and travel, office supplies, cafeteria operations

etc., are excluded. The boundaries of this study were set based upon the findings of previous studies which indicated that the majority of the carbon

impact of the semiconductor product life cycle is captured through examination of fabrication and use (5) (9).

The following subsections describe the methodology adopted to analyze the life cycle GHG emissions within the stated boundaries.

A. Fabrication

Semiconductors are fabricated on silicon wafers in fabrication facilities (fabs) through a photo-lithography process that includes a series of complex

steps. For this study, GHG emissions associated with fab operations were analyzed for two locations:

a) GLOBALFOUNDRIES, Dresden, Germany b) Taiwan Semiconductor Manufacturing Company (TSMC), Hsinchu, Taiwan

Scope I (direct emissions to the atmosphere) and Scope 2 (secondary emissions associated with the use of electricity) emissions data were collected

from the fabs making each of the dies included in this comparison. Emissions were allocated to each die based on the number of mask layers in the

Chemical

Manufacturing

Silicon Wafer

Production

Equipment

Manufacturing

Other upstream

inputs: e.g.,

transportation,

waste outputs

Semiconductor

Fabrication

200+ steps, 50+

unique processes

Energy, material

Input and

emissions are

tracked at the unit

process level

Die cutting, chip

packaging and

assembly

Microprocessor

Use

End of Life

Us

eD

isp

os

al

ManufacturingAssembly, Test

Marking and PackagingRaw Materials

Figure 3: Life cycle of semiconductors


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process and the size of the die. This allocation was based on previous studies which indicated that the amount of energy and fixed consumables used in

the fab are related to the number of mask layers and area of silicon processed (9). The number of mask layers also reflects the process complexity (5)

(10). Using this information, GHG emissions were allocated to each die using a process of normalization as described in ISMI Semiconductor Key

Environment Performance Indicators Guide (9).

For Scope 3 emissions, the boundaries were defined as raw materials input, output (waste, etc.) and transportation of raw materials to the fabrication

plants. Business travel, office supplies, capital goods, and other tangential factors were excluded from analysis. The Phenom Footprint Study

established that Scope 3 emissions were an estimated 16% of total fabrication phase GHG emissions (which in turn has been found to be approximately

10% of the overall carbon footprint). Given the small contribution of Scope 3 emissions, and assuming that there are no significant differences between

Scope 3 conditions in the Phenom Footprint Study and this study, we applied the Scope 3 emissions to this study.

B. Assembly

Assembly for these products occurs at multiple locations. For example, one of the die included in the study - RS880M - is assembled at two different

locations and tested at two other locations. This presents additional complexities for data collection. When reliable Scope 1 and 2 emissions data were

available for this stage of the life cycle, they were applied and normalized to the die as described in the ISMI Key Environmental Performance Indicators

Guidance (9). In instances where reliable data were unavailable, this study relied on the emissions estimates derived from the Phenom Footprint Study.

The boundaries for Scope 3 emissions for Assembly operations of the products included transportation of raw materials, raw materials input and output

(waste, slurry, etc.). Where information was available, raw materials input have been calculated and normalized. For the products for which the data was

unavailable, these emissions were estimated from the Phenom Footprint Study. This study established that Assembly accounted for less than 0.3% of

the total carbon footprint.

C. Test, Marking and Packaging (TMP)

Similar to the Assembly stage, TMP for the products occurs at multiple locations. For Scope 1 emissions, an estimate of the emissions was taken from

the Phenom Footprint Study. Scope 2 emissions, on the other hand, were readily available and measured from two of the locations. Scope 3 emissions

in this stage are primarily from raw material transportation, and emission estimates from the Phenom Footprint Study were applied. This study

established that TMP accounted for less than 7% of the total carbon footprint.

D. Retail and Distribution

This stage was excluded from the analysis for this study. The Phenom Footprint Study established that the transfer of product to warehouse distribution

centers has a negligible impact (about 1%) on the total life calculated GHG emissions.

E. Product Use

Previous studies have suggested that the product use is the most carbon intensive phase in the life cycle of a semiconductor microprocessor (11).

Various studies have shown that energy consumption during the use phase can account for up to 90% of the overall GHG emissions (12) (13)

The total energy consumed by the product is highly dependent on how it is used by the end consumer. Therefore, the usage profile becomes very

important in calculating the energy consumption associated with the products. Since the usage profiles vary considerably, there is no universally

accepted standard for measuring the use of PCs.

For the purposes of this study, several methodologies were considered to study the GHG emissions impact of the product use phase. One study used

internal company data to determine usage profiles, and assumed a lifetime of 5-6 years for notebooks (13). Another research paper employed a usage

model for residential and business users separately, and calculated the energy consumption for a lifetime of 2.5 years (10). Recently, Apple Inc.

conducted life cycle assessments of its products and used the consumptive patterns according to the European Commission and US Environmental

Protection Agency (EPA) eco-design agencies (14).

For this paper, product use was calculated using two standards: ECMA-383 (Measuring the Energy Consumption of Personal Computing Products) by

European Computer Manufacturers Association (ECMA) International (15), and the ENERGY STAR® Program Requirements for Computers v5.0 which

was created by the US EPA (16). These standards are used as benchmarks for energy-efficient design of consumer products (desktops, notebooks) and

for energy consumption calculations.

While ECMA and ENERGY STAR® are typically used to calculate the Total Energy Consumption (TEC) of an entire system like a Desktop or Notebook

computer, this paper applies these methods to estimate power use at the component level (e.g., processors, Southbridge, Northbridge) which is always

lower than the power coming from the wall outlet due to losses across the entire system. To estimate component level power, we converted “wall

power” (power coming from the wall outlet) by applying the relevant unit efficiencies (DC-DC conversion losses and AC adaptor efficiency).


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Duty Cycle Value Definitions

Toff 60% The percent time the product annually spends in the off mode

Tsleep 10% The percent time the product annually spends in the sleep mode

Tidle 3% The percent time the product is annually on and in the long idle mode (screen blanked)

Tsidle 17% The percent time the product is annually on and in the short idle mode (screen not blanked)

Twork 10% The percent time the product is annually on and in the active mode (screen not blanked)

a. ECMA-383 (Measuring the Energy Consumption of Personal Computing Products)

The ECMA-383 standard was used for the following reasons:

1) ECMA International is an industry association founded in 1961 and dedicated to the standardization of Information and Communication Technology

(ICT) and Consumer Electronics (CE). The ECMA-383 Standard is the latest in the series of standards that are being developed for

environmentally-conscious designs of ICT and CE industry products (published as recently as December 2009).

2) It clearly specifies a “majority profile” of users and calculation of annual TEC for use. (16) i. Majority Profile – Using a Business Workload

ECMA describes a “majority profile” of computer users based on computer use for business, home etc. (Figure 4). According to ECMA, the aim of using a majority profile is to estimate the total energy consumed - or TEC value - of a single profile which represents a “typical” user. The ECMA standard breaks down the majority profile into profile attributes defined

in Table 2iii. A duty cycle describes the percentage of time the product spends in

a particular mode over the course of a year. For example, a duty cycle of 60% for

the “off” state would mean that the system (PC) is off for 14.4 hours/day (60% x

24 hours/day) throughout the life of the PC. Similarly, statistical data has been

applied to determine percentage of time in the other duty cycles for a majority of

the notebook users (15). Table 2 shows the duty cycle attributes using the ECMA

standard for a majority profile (15).

ii. Limited profile study – Using a 3DMark workload A limited profile study is also described in ECMA with the duty cycles as listed in Table 3. Using the average power consumed during a workload

defined by 3DMark, an effort has been made to determine the use phase energy consumption for a system that would represent the energy use patterns

of a notebook used for high end gaming and graphics intensive workloads. The duty cycles shown in Table 3 represent a system which is on an active

workload for 2.4 hours/day, in sleep mode for 2.4 hours/day (screen not blank), idle (screen blank) for less than an hour/day and off for 14.4 hours/day

for the entire life of the system.

b. ENERGY STAR® - Program Requirements for Computers

ENERGY STAR® is a joint program of the U.S. Environmental Protection Agency and the U.S. Department of energy that helps encourage the use of

environmentally efficient products and practices. Similar programs have been adopted by Japan, New Zealand and the European Union. The ENERGY

Duty Cycle Value Description

Toff 60% The percent time the product annually spends in the off mode.



Tsidle 20% The percent time the product is annually on and in the short idle mode (screen not blanked)

Twork 0% The percent time the product is annually on and in the active mode (screen not blanked)

Figure 4: ECMA Majority Profile-a graphical representation

Table 2: ECMA Majority Profile – Duty cycles Table 3: ECMA Limited Profile with an active workload


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STAR® Program Requirements for Computers Version 5.0 outlines a “conventional profile” for annual energy consumption similar to the TEC defined in

the ECMA “majority profile.” (16). The ENERGY STAR® profile was used in addition to the ECMA standard for purposes of this study.

ENERGY STAR® Conventional Profile Like the ECMA-383 ”majority profile,” ENERGY STAR® V5.0 describes a

“conventional profile” of users which is based on usage pattern studies (16). Through

these studies, duty cycle estimates have been developed. These duty cycles are

relevant for both residential and commercial computers (16).

The duty cycles for the ENERGY STAR® “Conventional Profile” are shown in Table 4.

c. Life of the Product The average life of a processor is another key variable in calculating total energy use.

In addition to the previously referenced studies which assumed lifetimes of 5-6 and

2.5 years for notebooks, two other studies were reviewed for selection of notebook

lifetimes. According to a Forrester report, the PC refresh rate for notebooks is a little

less than 4 to 5 years (17). Recently (2010), Apple Inc. conducted an environmental

footprint of one of its notebook products, and assumed a life of 4 years (14). For the

purposes of this study, the average lifespan of the PC (and therefore the processors)

is estimated conservatively at 3.5 years. This lifespan was selected to avoid

overstating the comparative climate benefits of the APU platform which increases with

increasing lifespan.

Energy Consumption calculations

Once the usage profile and the average life of the product are determined, the annual energy consumption is calculated as (15):

Notebook TECestimate - the total energy consumption by the component of the Notebook (APU, Southbridge, Graphics card etc) Toff - percent time the product annually spends in the off mode

Tsleep - percent time the product annually spends in the sleep mode

Tidle - percent time the product is annually on and in the long idle mode (screen blanked)

Tsidle - percent time the product is annually on and in the short idle mode (screen not blanked)

Twork - percent time the product is annually on and in the active mode (screen not blanked)

Toff + Tsleep + Tidle + Tsidle + Twork = 100%

Poff, Psleep, Pidle, Psidle, Pwork are the average power in W consumed in the off, sleep, idle, short-idle, and active work modes respectively

- Conversion factor applied to convert from hourly to annual energy consumption Once the TECestimate is calculated, it is multiplied by the life of the product to obtain the average energy consumption over the life of the product.

The next step is to convert the use phase energy into units of carbon dioxide emissions. For this purpose, an emission factor of 7.18 x 10-4 metric tons

CO2 / kWh is applied. This emission factor was obtained from the eGRID2007 Version 1.1, U.S. annual non-base load CO2 output emission rate, and

year 2005 data from the EPA Greenhouse Gas Equivalencies Calculator (18).

F. End of Life

Little information is available about the GHG impacts associated with the end of life disposal of electronic products (11). Therefore, this phase is outside

the scope of this paper and excluded from analysis.

Once the boundary conditions were established and the methodology was defined, a carbon footprint analysis model was constructediv. In this model,

the GHG emissions impact of each product was calculated and expressed in equivalent kilograms of carbon dioxide (kgCO2e). By adding up the GHG

emissions for each of the chips, we calculate the total GHG emissions over the life of the APU reference system and Nile reference system. The findings

of the analysis are presented in the following section.

Duty Cycle Value Description

Toff 60% The percent time the product annually spends in the off mode



Table 4: ENERGY STAR® Conventional Profile – Duty Cycles


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VII. Findings

The major findings of this paper revolve around two things: 1) the comparative GHG emission savings achieved with the APU reference system as

opposed to the Nile reference system, and 2) the importance of product use phase as the major driver of these savings. In addition to the product use

stage, the only other stage analyzed as part of this study and shown to have a notable impact in the product life cycle was the fabrication phase. The

findings of the product use and fabrication stages will be presented.

While we evaluated both EPA ENERGY STAR® and the ECMA standard for estimating computer usage patterns, we used the ECMA-majority profile for

the overall comparison. We selected the ECMA-383 standard because the “majority profile” closely reflects the way the majority of people actually use

their computers (15). For completeness, we also analyzed the variance in the overall results using both of these standards. This will be discussed later in

the paper.

The overall APU Reference system and Nile Reference system comparison of GHG emissions is presented in Figure 5. These findings show that the

GHG emissions of the APU Reference system is approximately 40% lower than the Nile Reference system. Results for each of the life cycle stages

within the scope of this study are shown in Table 5. Similar to the findings of previous studies, the product use phase is the highest contributor to the

carbon footprint (around 84%). (Figure 6).

The product use phase is also the point at which the Nile Reference system and APU

Reference systems begin to significantly diverge. Throughout fabrication, Assembly

and TMP phases, the quantitative difference in GHG emissions is negligible. As

shown in Figure 7, the total difference in carbon emissions (∆) is driven almost

entirely by the use phase. In the following subsections, we discuss the findings for

two major stages – Fabrication and Product Use.

A. Fabrication phase GHG emissions results

Comparison of the GHG emissions in the fabrication stage shows that the APU reference system results in about 46% less than the Nile Reference

system (Figure 8). Much of these savings can be accounted for by the reduction in the number of chips (from 4 in Nile reference system to 2 in APU

reference system).

In the fabrication phase, Scope 2 (electricity use) accounts for the largest amount of GHG emissions (Figure 9). This is roughly consistent with the

findings of other studies where it was established that electricity contributed around 60% of the carbon emissions within the fabrication stage (13).

Process Step “APU” reference system

“Nile” reference system

Wafer Fab 9.6% 10.7%

Assembly 0.1% 0.3%

TMP 7.0% 5.5%

Use phase (kgCO2e) 83.3% 83.6%

Total 100.0% 100.0%

Table 5: Percentage contribution of the different stages of the

microprocessor lifecycle on the overall carbon footprint

Wafer Fab10.7%

Assembly0.3%

TMP 5.5%

Use phase83.6%

3.90.0 2.8

33.540.2

7.20.2 3.7

56.3

67.4

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

kgC

O2

of

GH

G e

mis

sio

ns

"APU" reference system

"Nile" reference system

Table 5: Percentage carbon footprint contribution of the different stages of the microprocessor life cycle

Figure 5: Total GHG emissions comparison of the two reference systems

Figure 6: GHG emissions breakdown resulting from the Nile reference system

Figure 7: Variance of GHG emissions resulting from the two reference systems


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Figure 9: Fabrication phase-breakdown of GHG emissions resulting from the APU Reference System

Figure 8: Fabrication phase-comparison of GHG emissions resulting from the two reference systems

Figure 10: EMCA Majority Profile – Use phase comparison of GHG emissions

B. Product Use

Using the ECMA majority profile, we

found that APU reference system

had 40.5% less use phase GHG

emissions as compared to Nile

reference system. The APU

reference system had approximately

23 kgCO2e less GHG emissions as

compared to the Nile reference

system. (Figure 10)

Using the ENERGY STAR®

conventional profile, a similar trend

was observed even though the use

assumptions varied considerably.

Figure 11 shows that the APU

reference system resulted in about

20 kgCO2e less emissions as

compared to the Nile reference

system. Though the absolute

emissions impact differs with the results of ECMA-majority profile, the percentage emissions

savings (36.3%) closely mirrors that in the ECMA majority profile analysis (40.5%).

Figure 12 shows the GHG estimates using the ECMA standard limited profile study (with an

active 3DMark workload of 2 hours/day). It was found that the use phase GHG emissions

increased considerably for the two reference systems owing to the higher energy

consumption in the active workloads. Once again however, the APU reference system

demonstrated considerable GHG emissions savings when compared to the Nile reference

system.

An additional comparison was conducted using the ECMA standard limited profile (active

workload) with variations on the time the system spent in active work mode. Assuming an

active workload of various time durations (0 hours/day, 0.5 hours/day, 1 hour/day and so on),

the use phase GHG emissions for the two reference systems were plotted (Figure 13). The

use phase GHG emission estimates of the two reference systems vary considerably with the

changes in workload. Notably, the absolute difference in the use phase GHG emissions

between the two systems (∆)

increased proportionally to

increases in the workload. For the

highest workload of 4.5 hours/day,

the absolute difference in use

phase GHG emissions over the

life of the reference systems was

69.6 kgCO2e. This represents a ∆

use phase savings of around 52%

for the APU reference system

versus the Nile reference system.

An additional simulation was

conducted to study the effects of

changing workload durations (duty

cycles) on the net use phase GHG

savings (%∆) associated with the

APU reference system. A

simulation modelv was constructed, and the time duration of active, idle and short idle modes were varied for 10,000 simulations with Twork values ranging

from 0% to 10%, thus resulting in 10,000 different usage profiles. As shown in Figure 14, the results demonstrate that the use phase GHG emission

savings for the APU reference system vary from 36% to about 56%. The median use phase savings from all the simulated results was close to 42%.

This is a strong indicator of the high use phase emission savings (%∆) associated with the APU reference system compared to the Nile reference

system even with changing usage profiles.

3.9

7.2

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0



kgC

O2

e G

HG

em

issi

on

s

∆=45.8%

48.5

97.5

0.0

20.0

40.0

60.0

80.0

100.0

120.0



kgC

O2e

GH

G e

mis

sio

ns

∆ = 50.3%

33.556.3

0.0

10.0

20.0

30.0

40.0

50.0

60.0



kg

CO

2e

GH

G e

mis

sio

ns

∆=40.5%

34.253.8

0.0

10.0

20.0

30.0

40.0

50.0

60.0



kgC

O2e

GH

G e

mis

sio

ns

∆=36.3%

Scope 1 emissions

24%

Scope 2 emissions

60%

Scope 3 emissions

16%

Figure 11: ENERGY STAR® Conventional Profile -

Use phase GHG emissions comparison of the two

reference systems

Figure 12: ECMA limited profile study – Use

phase GHG emissions comparison of the two

reference systems using active workload


9

i. Variance Analysis – a discussion on the effect of varying workloads (time) on Total GHG emissions

The use phase emissions vary linearly with the changes in workload (varying time

durations of active workloads) (Figure 13). The total emissions also follow a similar

pattern (Figure 15) where the total emissions vary considerably with the varying

workloads. This absolute difference in the emissions of the two reference systems

(Total ∆) is a linear function of workload. An analysis of percentage savings (% ∆)

associated with the APU reference system shows that the net savings (%∆)

increases with increasing active workload duration (Figure 16). From all the

scenarios above, we conclude there is a significant GHG emissions savings benefit

associated with APU reference system that increases with increasing use and life

of the product.

The lower carbon footprint for the APU reference is largely due to efficiencies

resulting from the integration of the CPU and GPU on a single chip. Combining

CPU cores, GPU cores and the Northbridge (the part of the chip where the

memory controller and PCI-express interfaces reside) onto a single piece of silicon

(4) eliminates a chip-to-chip linkage that can add latency to memory operations

and consume power. It takes less energy to move electrons across a chip than to

move those same electrons between two chips. The power saved by this change

can significantly increase system energy efficiency and help to reduce the

system’s overall carbon footprint without compromising performance. The co-

location of key elements on one chip also allows for a holistic approach to power

management on the APUs. For example, various parts of the chip can power up

and down depending on workloads, which in the aggregate, can amount to

significant power savings.

VIII. Implications

Today’s semiconductor devices are extremely energy efficient. To put things in

perspective, a 60W light bulb is responsible for around 43 gCO2e emissions/hourvi.

In comparison, this study predicts that an APU reference system is responsible for

only 1.1 gCO2e/hour under typical user conditionsvii

. Based on this study, an

average consumer could save up to 9 KWh of energy/year and without significantly

compromising computing performance when using an APU powered system as

compared to the Nile powered system with discrete graphics. For an enterprise

customer, this would mean that a business with 5,000 employees using the APU

reference system could save up to 45 MWh/year in electricity.

Figure 14: Probability density of use phase GHG emissions savings (%Δ) with variation in active, idle and sidle duration for a simulation of 10,000 random user profiles

Figure 13: Variance in use phase GHG emissions with changes in time duration of active workload

Figure 15: ECMA standard with active workload - Variance of total emissions resulting from the two reference systems with different durations of active workload

Figure 16: Percentage comparison of the total emissions resulting from the two reference systems with variance in time duration of active workloads

33.5 36.7 39.9 43.0 46.2 49.4 52.6 55.7 58.9 62.1

56.364.7

73.181.4

89.898.2

106.6115.0

123.3131.7

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

kg

CO

2e

of

em

iss

ion

s

Hours of active workload/day

"APU" reference system Use phaseemissions (kgCO2e)

"Nile" reference system Use phaseemissions (kgCO2e)

34% 36% 38% 40% 42% 44% 46% 48% 50% 52% 54% 56%

Pro

bab

ilit

y D

en

sit

y(n

um

ber

of hits)

Use phase emission savings (%)

Use phase emissions-net savings (%)

40.3%

42.7%

44.6%

46.2%

47.5%

48.7%

49.6%

50.4%

51.2%

51.8%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Hours of active workload/day

∆ (%) in Total emissions resulting from the two reference systems


10

Assuming APUs are used in 6 million notebooks in North America, this would translate to a potential energy savings of about 54,000 MWh/year of

electricity from product use alone compared with the Nile reference system. These savingsviii

could be enough to fulfill the yearly electricity needs of

around 4,706 residential homes in North America or the equivalent of GHG emissions from 7,415 cars in one year (18).

From a raw materials standpoint, an APU reference system delivers roughly equivalent performance utilizing a total die size that is 60% less than the

Nile reference system.

IX. Discussion

In this study, we established that the emission values (kgCO2e) change considerably with varying workloads, and the APU reference system achieves

significant carbon emissions savings as compared to the Nile reference system across all workload scenarios (Figure 16). This study specifically

focused on a comparison of products rather than the absolute value of GHG emissions. We feel that this approach is more realistic for PC users who

may choose to upgrade their system to the new APU technology. This study relies heavily on work done in previous life cycle GHG studies such as the

Phenom Footprint Study. Because life cycle assessment is a fairly complicated process, it is difficult to gather accurate data for each stage in the

process. However, given the dominance of the use phase in the overall GHG footprint, we feel that reliance on prior work for some of the less impactful

stages while focusing on the use phase is warranted.

A standardized approach for the product use phase for semiconductor products would be useful for future similar studies. In this analysis, we found that

variations in key parameters (use profiles and computer lifespans) have a significant impact on the overall results, and total emissions vary almost

proportionally with use phase emissions. According to one study, reducing power consumption in the use phase is the most effective way to limit overall

environmental impacts for the more recent generations of logic chips (11). ECMA-383 has taken some valuable steps in this direction by establishing a

majority profile. Going forward, this majority profile should be further refined and studies should be performed to ascertain the majority profile for different

categories like home, office, workstations, and different categories of users like gamers, designers etc. Once this is established, it would become easier

to compare the environmental impact of various consumer electronics and semiconductor products.

Lastly, this study only analyzed the GHG footprint of semiconductor devices, not their overall GHG benefits. Recent studies (19) have concluded that

the application of digital technologies can produce far greater benefits to the goal of climate protection than the GHG impacts associated with their

production and use.

X. Limitations

This study is based on the initial estimates available for the product compared. At various stages, where data were not available, estimates based on previous studies were utilized. Some examples of these estimates are:

1. Fabrication data – At the time this study was prepared, APUs had still not gone into mass production. Therefore, this study uses

representative numbers from 2009 for production data of current generation CPUs and GPUs.

2. Power consumption data – At the time of this study, APUs were still undergoing power optimization. Therefore, the available power

measurement results did not reflect an optimized system and further improvement is expected.

3. Assembly, Testing, Marking and Packaging data - The study compares the footprint of six individual chips for which the assembly and

testing conditions are different. Assembly, Test, Mark and Packaging (ATMP) takes place in various facilities across the world. Data

were not available from all ATMP facilities and estimations were made in some cases.

4. Choice of Benchmarks - For the comparison of products, this study relied upon two performance benchmarks: PCMark®, 3DMark. While

we believe that these benchmarks reflect the real life usage models, others use scenarios are also plausible and could materially affect

the results of this study.

XI. A Note about the Author

Siddharth Jain completed an AMD internship at AMD in the summer of 2010 during which time he completed this paper. He relied heavily on internal AMD research completed by Product Marketing and Corporate Responsibility and other groups at AMD.

XII. Acknowledgements

The author would like to sincerely thank Tim Mohin, Donna Sadowy, Brett Stringer, Erwin Hans, John Taylor, Jonathan Seckler, Silke Hermanns and Catherine Greenlaw for their valuable guidance throughout this study.


11

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12

i Figures 1 and 2 were developed by AMD for internal reference. ii The AMD Phenom study was a Business to Business (B2B) carbon footprint study and ended at the stage where the product (processor) reaches the

customer (Original Equipment Manufacturers). It excluded stages like the Retail and Distribution, Product Use, End of Life etc. iii Source: Select information in Table taken from the ECMA 383- Measuring the Energy Consumption of Personal Computing Products. Twork duty cycles

were combined with the short idle duty cycle as the profile TEC error was small and the short idle power would be used to as a proxy for Twork power iv A model framework was constructed (using Microsoft Excel) for the comparative assessment of all the products. For each of the stages described (in

methodologies section), analysis was done. The combined results were obtained. Important parts of the results are presented in the Findings section. The spreadsheet contains sensitive data and requires a Non-Disclosure Agreement to be signed before it can be obtained. v The simulation model was constructed using the @Risk modeling tool, a Microsoft excel tool created by Palisade Corporation (www.palisade.com/risk)

vi Assuming the average an emission factor of 7.18 x 10-4 metric tons CO2 / kWh (obtained from the eGRID2007 Version 1.1, U.S. annual non-base

load CO2 output emission rate, and year 2005 data from the EPA Greenhouse Gas Equivalencies Calculator), then the emissions from a 60 W light bulb is 718 gCo2/kWh*.06 KW = 43 gCo2 e emissions per hour. vii

As calculated by Total Energy consumption using ECMA International standard- majority profile. Using this profile, emissions resulting from the APU

reference system over the life of the product (assumed to be 3.5 years) is 33.5 kgCO2e. Calculating the hourly rate 33.5/(3.5*8760) =1.1 gCO2e/hour. viii

Assuming average retail price of electricity of residential sector in the state of California (source: US Energy Information Administration,

http://www.eia.doe.gov/electricity/epm/table5_6_a.html)

http://www.eia.doe.gov/electricity/epm/table5_6_a.html