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AN ENVIRONMENT FOR ENERGY CONSUMPTION ANALYSIS OF

CACHE MEMORIES IN SOC PLATFORMS

Cordeiro, F.R.; Silva-Filho, A.G.; Araujo, C.C.; Gomes, M.; Barros, E.N.S. and Lima, M.E.

Informatics Center (CIn)

Federal University of Pernambuco (UFPE)

Av. Prof. Luiz Freire s/n Cidade Universitria Recife/PE - Brasil

email: { frc, agsf,cca2,maag,ensb,mel}@cin.ufpe.br

ABSTRACT

The tuning of cache architectures in platforms for

embedded systems applications can dramatically reduceenergy consumption. The existing cache exploration

environments constrain the designer to analyze cache

energy consumption on single processor systems and

worse, systems that are based on a single processor type. In

this paper is presented the PCacheEnergyAnalyzer

environment for energy consumption analysis of cache

memory on SoC platforms. This is a powerful energy

analysis environment that combines the use of efficient

tools to provide static and dynamic energy consumption

analysis, the flexibility to support the architecture

exploration of cache memories on platforms that are not

bound to a specific processor, and fast simulation

techniques. The proposed environment has been integratedinto the SoC modeling framework PDesigner, providing a

user-friendly graphical interface allowing the integrated

modeling and cache energy analysis of SoCs. The

PCacheEnergyAnalyzer has been validated with four

applications of the Mediabench suite benchmark.

1.INTRODUCTIONCurrently, the energy consumed by the memory hierarchy

can account for up to 50% of the total energy spent by

microprocessor-based architectures [1][2]. This fact

becomes more critical due to the emergence of SoCs,

meaning a large part of the integrated circuits contains

heterogeneous processors and often cache memories.

Moreover, current semiconductor technologies have raised

static memory consumption from negligible to up to 30%.

Many approaches do not take this fact in consideration.

Many efforts have been made to reduce the

consumption of energy by adjusting cache parameters to the

needs of a particular application[3][4][5][6]. However,

since the fundamental purpose of the cache subsystem is to

provide high performance for memory accessing, cache

optimization techniques are supposed to be driven not only

by energy savings but also by preventing degradation of the

applications performance.

No single combination of cache parameters (total size,

line size and associativity), also known as cache

configuration, would be perfect for all applications.

Therefore cache subsystems have been customized in order

to deal with specific characteristics and to optimize theirenergy consumption when running a particular application.

By adjusting the parameters of a cache memory to a

specific application it is possible to save on average 60% of

energy consumption [3].

Nevertheless, finding a suitable cache configuration

(combination of total size, line size and associativity) for a

specific application can be a complex task and may take a

long time for simulation and analysis. Most of the tools use

exhaustive or heuristics based exploration [4][5][6] of all

possible cache configurations. These are cost intensive

approaches, leading to unacceptable exploration times.

The tools for cache analysis lack the resources designers

need to perform an energy consumption analysis withefficacy and efficiency. Some do not take into consideration

static energy consumption. These tools are not flexible;

they are normally bound to a specific processor. Designers

experience several difficulties when they need to analyzecaches on a platform with processors that are different from

the ones in the tools.

Some environments have been developed aimed at

cache parameters exploration [4][5][7]. However, none of

them has taken into account cache memory energy models

that consider the two energy components: static anddynamic. Silva-Filho [6] considers static and dynamic

energy components, however, the work focuses on a single

platform and is not integrated in to a graphical interfaceenvironment for platform analysis.

Although cache memory exploration considering energy

consumption is not a new issue in Design SpaceExploration (DSE), this work contributes with a new

approach for exploiting platforms with cache memory

architectures, considering energy consumption. Differently

from other approaches with analysis intended for only one

processor, this paper presents an environment for energy

consumption analysis called PCacheEnergyAnalyzer. Thisis a an environment that provides support for the

exploration of cache memories configurations in terms of

static and dynamic energy consumption. Moreover, it uses a

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fast exploration strategy based on single-step simulation for

simulating multiple sizes of caches simultaneously. It also

supports the cache exploration in platforms not bound to a

specific processor. All these features are integrated in a

easy to handle graphical environment in the PDesignerFramework.

The rest of this paper is structured as follows. In the

next section, we discuss some recent related work. In

section 3 the proposed approach for a cache energy

environment is presented. In section 5 some results are

presented comparing the potentialities for two different

processors and several applications by using

PCacheEnergyAnalyzer environment. Finally, in Section 5

the conclusions and future directions are discussed.

2.RELATED WORKSome existing methods still apply the exhaustive search to

find the optimal cache configuration in the design space.

However, the time required for such an exhaustive search

is often prohibitive. Platune [8] is an example of a

framework for adjusting configurable System-on-Chip

(SoC) platforms that utilizes the exhaustive search method

for one-level caches and just one type of processor (MIPS

core processor). It is suitable only in some cases when

there are only a small number of possible configurations

[8]. But for a large design space, a long exploration time

would be required. Even the use of heuristics may be

unsuitable for several long simulations.

Palesi et al. [9] reduces the possible configuration spaceby using a genetic algorithm and produces faster results

than the Platune approach. Zhang et al. [3] have developed

a heuristic based on the influence of each cache parameter

(cache size, line size and associativity) in the overall

energy consumption. However, the simulation mechanism

used by the previous approaches is based on theSimpleScalar [10] and CACTI tools [11]. SimpleScalar is a

microprocessor simulation tool based on command lines,

which generate the results of the applications

performance. The CACTI tool is intended to generate

energy consumption per access for a given cache

configuration. In these cases, the simulation of different

configurations for the same application may take a longperiod.

Prete et al. [12] proposed the simulation tool called

ChARM for tuning ARM-based embedded systems that

also include cache memories. This tool provides a

parametric, trace-driven simulation for tuning systemconfiguration. Unlike previous approaches, it provides a

graphical interface that allows designers to configure the

components parameters of the components, evaluate

execution time, conduct a set of simulations, and analyze

the results. However, energy results are not supported by

this approach.

On the other hand, Silva-Filho, in [8] takes into account

static and dynamic energy consumption estimates in his

analysis with the TECH-CYCLES heuristic. This heuristic

uses the eCACTI [13] cache memory model to determine

the energy consumption of the hierarchy. The eCACTI,differently from other approaches, considers the two

energy components: static and dynamic. The static energy

component that was negligible in previous technologies

represents, for recent technologies, up to 30% of the

energy in CMOS circuits [14].

The eCACTI is an up-to-date cache memory model that

was extended from the original CACTI model [11]. The

original CACTI tool does not consider the static

component of energy. Also, the transistor width of various

devices is assumed to be constant (except for wordlines)

when analyzing power and delay. Nowadays this

assumption would be incorrect [11], because the transistor

widths in actual cache designs change according to theircapacitive load. These lead to significant inaccuracies in

the CACTI power estimates.

The PDesigner framework is an Eclipse-based

framework [15] that provides support for the modeling and

simulation of SoCs and MPSoCs platforms. By using this

framework the platform designer can build the platform

graphically and generate an executable simulator.

Currently, PDesigner is a free solution and offers support

to modeling platform with different components such as

processors, cache memory, memory, bus and connections.

Performance results are obtained from this approach;

however, energy results are not supported.

Looking at the situation depicted in Table 1 it becomes

evident that there is no environment that combines theflexibility to model multiple platforms with caches; the use

of an approach based on a single simulation; the capability

to estimate both dynamic and static energy consumption of

cache memories; or the possibility to explore the platform

configuration design space graphically.

Table 1. Comparison of related studies.

Multi

Platform

Modeling

SingleSimul.

DynamicConsump

.

StaticConsump

.

GraphicalExploration

Zhang - - - -

Palesis - - - -

Silva-Filho - - -

Platune - - -

SimpleScalar - - - - -

ChARM - - - -

PDesigner - - -

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LibraryExtension

Integrationin PDesigner

VisualEnvironmentInteraction

AnalysisFlow

LibraryExtension

Integrationin PDesigner

InteractiveGraphicalEnvironment

AnalysisFlow

VisualEnvironmentInteraction

Dynamic & Static

Energy estimation

PCacheEnergyAnalyzer Plugin

3.PROPOSED APPROACHIn this paper, we propose the development of a cache

energy consumption estimation tool that implements an

energy consumption analysis flow and its integration as aplugin in the PDesigner framework. The plugin, called

PCacheEnergyAnalyzer, provides dynamic and static

energy consumption statistics for cache memory

components of a SoC. The plugin is also an interactive

environment that provides a graphical user-friendly

interface for cache analysis and its interaction with theplatform model already provided by the PDesigner.

The proposed approach is depicted in Figure 1. The first

step in the approach has been the definition of an energy

cache analysis flow. For the implementation of the flow a

new SystemC component that generates traces of memoryaccesses has been created, and that has been added to the

PDesigner library. Moreover, two additional tools have

been created: an interactive graphical environment that

allows the control and view of the results of the analysis;

and a tool for dynamic and static energy consumption

estimation based on the eCACTI model. These two toolscomprise the PCacheEnergyAnalyzerplugin. The plugin

allows the designer to select a cache on the platform,

define the design space to be explored, visualize the results

in charts, select the desired cache configuration from the

chart and reflect the decision on the platform.

Finally, the updated library and thePCacheEnergyAnalyzer plugin have been integrated into

the PDesigner framework. The result is a powerful tool

that supports the modeling of platforms and the cache

architecture exploration.

In the rest of this section the analysis flow, its

implementation by the PCacheEnergyAnalyzer and the

integration in the PDesigner are explained.

Fig. 1. Proposed approach.

3.1.Cache Energy Consumption Analysis FlowFigure 2 shows the flow used to analyze energy

consumption in cache memories. All necessary steps are

detailed carefully in this section.

Fig. 2. Energy consumption analysis flow.

Initially, the desired platform is graphically constructed

from a list of components available in the PDesigner

component library. System designers model the

architecture by dragging and dropping the components

from the component palette. The component palette has the

following component types: processor, bus, device,

memory and cache memory. Figure 3 shows an example of

a platform composed of a MIPS processor, cache memory,

bus and main memory. The component master and slave

protocol ports are connected through connections. The

designer can also change the component parameters by

selecting them and using the properties view (lower part of

the Figure 3).

The application is a binary code compiled for the target

processor. The designer selects the processor and

associates the binary file with the triple {processor,

memory, load address}.

In order to make energy analysis in cache memory it is

necessary to select the PCacheEnergyAnalyzer option

when the designer right-clicks on the cache component.

This option enables the platform to explore energy

consumption in the cache memory component.

Once the cache component has been selected, the

designer can change the cache memory properties. In theProperties window shown in Figure 3, the designer can

change the exploration space of the cache memory

component. This is done by defining minimum and

maximum values for each cache memory parameter. The

parameters are the following: cache size, cache line size

and associativity. For the associativity there is only the

maximum parameter.

After, an executable simulator of the platform it is

generated. The simulator performs a single simulation and

generates miss and hits statistics for the entire

configuration space defined by the designer. So, the result

of the simulator execution is an XML file that contains the

Define

Simulate

Define

View Results

Select Configuration

Update Platform

Select Cache Calculate Energy

Platform

Mapping

Application

Energy Analysis

Exploration Space

Configuration Space

DefineTransistor Technology

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cache configuration ID, cache parameters such as size, line

size, associativity, number of accesses and miss rate.

A simulation mechanism using a single-pass simulation

technique, based on [16] work, has been adopted. Usually,

simulations using this method are based on traces andspend more than one single simulation [16] [17]. For

instance, single-pass cache evaluation mechanism

proposed in [16] is 70 times faster than a simulation-based

mechanism for ADPCM application from Mediabench.

Fig. 3. PDesigner, Architecture Modeling, Component

Palette and Configuration Space.

The exploration space may contain cache

configurations that are invalid or that are not interesting for

the designer. After simulation, the designer is able to select

some or all configurations for energy analysis and definethe configuration space that contains all the desired cache

configurations through a Configuration Selection Window.

This window allows the designer to select the transistor

technology size and also all the cache configurations in the

configuration space. After the configuration space has been

defined, the energy module calculates the energyconsumption and number of cycles for each selected

configuration.

The cache memory energy consumption calculation

flow is depicted in Figure 4.

A parser receives as input the selected

Configurations Space saved in the XML file and separates

it in two sets of information. The first of these is the cache

parameters and technology information that are provided to

the eCACTI tool for the dynamic and static energy

calculation per access. The second one contains the

number of misses, the number of accesses and cacheparameters of the chosen configuration. This information,

together with the dynamic and static energy provided by

the eCACTI, is used to calculate the total static and

dynamic energies consumed by the cache memory for the

application. In addition, in this step the total number ofcycles needed to run the application is also calculated.

Once calculated these parameters, another parser generates

the energy estimation results for each configuration also in

XML format file.

Fig. 4. Energy Calculation Flow

A cost function represented by F = Energy x Cycles

equation is also calculated. The minimization of this cost

function makes it possible to obtain the cache

configurations near to Pareto-optimal [8]. These cacheconfigurations present a tradeoff between performance and

energy consumption. The configuration that has the lowest

Energy x Cycles cost is also identified.

Once the energy calculation flow is concluded, the user

graphically visualizes the results of the cache energy

analysis. The energy consumption estimation for each ofthe configurations in the configuration space is displayed

in a visual interactive chart as depicted in Figure 5. Thechart displays on the y-axis the energy consumed and, on

the x-axis, the performance in number of clock cycles.

Each point on the chart corresponds to one of the

configurations in the configuration space.

The chart is interactive, meaning the user can select one

of the points and display information about it. There are

two types of information: the first, in the form of a tool tip,

is depicted by the rectangle in Figure 5 and contains the

number of cycles and energy consumed by the selected

configuration; the second form of presenting information is

by viewing properties, also shown in Figure 5.

Selected Configurations

Space (.XML)

parser

Cache parameters

and technology

eCACTI

Energy, Cycles

CalculationEnergy, Cycles

Results

parser

Energy Consumption

Estimation Results (.XML)

Cache parameters,

# Miss, # Accesses

Dynamic and Static

Energy per access

Processor

Cache Memory

Bus

Main Memory

Component

Exploration Space

Processor Load Address

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Fig.

Here the following information is di

configuration parameter values, miss

accesses, the cost value based on t

calculation, dynamic and static energy

total cycles required to run the applica

energy consumption.

The configuration with the lowest

represented in the interactive chart in a d

user can use this configuration as a re

he/she is not obliged to choose it

configuration.

The user also can interact with the chart iproperties of a particular cache configurthe designer selects one of the configu

his/her performance/energy consumption

user selects the configuration by simpl

point in the chart. In this step, the de

platform by replacing the actual cache cselected configuration parameter

PCacheEnergyAnalyzer plugin makes

automatically by interacting with

Framework.

4.RESULTS

The PCacheEnergyAnalyzertool has be

the cache memory design space f

applications of the Mediabench benchm

timing, rawcaudio and rawdaudio.

The architecture is composed of o

structure SimpleBus; one cache mem

memory. The parameters of the cache

and the exploration is performed for

processors and four different applic

Mediabench suite [18].

0,0000

0,0200

0,0400

0,0600

0,0800

0,1000

0,1200

Timing Rawca

Energy

(Joules)

. Energy estimation interactive chart.

played: the cache

rate, number of

he cost function

consumption, the

tion and the total

calculated cost is

ifferent color. The

erence. Therefore

as the optimal

order to view theation. In this step,rations that meets

requirements. The

y clicking on the

igner updates the

mponent with thevalues. The

the substitution

the PDesigner

n used to explore

r four different

ark suite [18]: fft,

ne interconection

ry; and a RAM

emory are varied

the two different

ations from the

The configuration space

configurations for each

technology was 0.18um. The

8192 bytes; the cache line siz

and the associativity ranges fr

The energy consumption

have been calculated based o

4. The results are then displ

interactive chart of Figure 5.

Fig. 6. Energy estimation

Figure 6 summarizes the e

values of the cache configur

and configurations with the l

the configuration space for e

MIPS and SPARCV8 process

Despite these two process

compilers and compilation

differences in some cases. It

the MIPS processor prese

consumption than the S

application, and slightly high

other applications.

dio Rawdaudio FFT

MIPS (Cost Function)

MIPS (Lowest Energy)

SPARC (Cost Function)

SPARC (Lowest Energy)

used considers 50 different

application. The selected

cache size varies from 256 to

e ranges from 16 to 64 bytes;

om 1 to 4.

estimation and performance

n the flow depicted in Figure

yed in the energy estimation

for different applications.

ergy consumption estimation

tions with best cost function

owest energy consumption in

ch application, running in the

ors.

rs have similar architectures,

optimization presents some

can be seen in the chart that

nts a much better energy

PARCV8 for the timing

r energy consumption for the

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Additionally, the proposed approach also was compared

with existing work by using the basicmath_small from

Mibench suite [19]. SimpleScalar and

PcacheEnergyAnalyzer(PCEA) were compared in terms of

fidelity by analyzing the energy consumption for somedifferent cache configurations. Each pixel in Figure 7

represents the energy consumption for a given cache

configuration (cache size, cache line size, associativity).

Fig. 7. Normalized Energy comparison for SimpleScalar

and PCEA approaches.

Although SimpleScalar tool do not support energyconsumption analysis, it was calculated with an approach

based on Zhang work [3], using one level cache and the

eCACTI cache memory energy model. For simplicity of

the analysis, data and instructions caches configurations

are assumed to be the same.

Results showed in Figure 7 indicate that both approachespresent fidelity. We believe that the precision difference

depicted in the figure 8 is due to the used compilers and

compilation optimizations.

5.CONCLUSIONIn this work has been presented thePcacheEnergyAnalyzer

environment for energy consumption analysis. The tool

provides support for cache memory energy consumption

estimation on SoC platforms. Initial studies were focused

for one level caches, however, it can be easily extended formore levels. Results have shown that it is a powerful tool

for helping users to find interesting cache configurations

for a particular application, which consider not only

performance, but also the best relation between

performance and energy consumption.

PCacheEnergyAnalyzer fills the gaps of the existing

tools by simultaneously providing multiplatform support,

extensibility, dynamic and static energy consumption

estimation and a graphical environment.

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