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A Hybrid Energy-EstimationTechnique for Extensible

Processors

Fei, Y.; Ravi, S.; Raghunathan, A.; Jha, N.K.

IEEE Transactions on Computer-Aided Design of

Integrated Circuits and Systems

Volume: 23 Issue: 5

Pages: 652-664

May 2004

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2005/7/13 A Hybrid Energy-Estimation Technique for Extensible Processor 2/24

Abstract

In this paper, we present an efficient and accurate methodology forestimating the energy consumption of application programsrunning on extensible processors. Extensible processors, whichare getting increasingly popular in embedded system design, allow

a designer to customize a base processor core through instructionset extensions. Existing processor energy macromodelingtechniques are not applicable to extensible processor, since theyassume that the instruction set architecture as well as theunderlying structural description of the micro-architecture remain

fixed. Our solution to the above problem is a hybrid energymacromodel suitably parameterized to estimate the energyconsumption of an application running on the correspondingapplication-specific extended processor instance, whichincorporates any custom instruction extension. Such acharacterization is facilitated by careful selection ofmacromodelparameters/variables that can capture both the functional andstructural aspects of the execution of a program on an extensibleprocessor.

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Abstract (cont.)

Another feature of the proposed energy characterization flow is theuse ofregression analysis to build the macromodel. Regressionanalysis allows for in-situ characterization, thus allowing arbitrarytest programs to be used during macromodel construction. We

validated the proposed methodology by characterizing the energyconsumption of a state-of-the-art extensible processor (TensilicasXtensa). We used the macromodel to analyze the energyconsumption of several benchmark applications with custom

instructions. The mean absolute error in the macromodel estimatesis only 3.3%, when compared to the energy values obtained by acommercial tool operating on the synthesized register-transferlevel (RTL) description of the custom processor. Our approach

achieves an average speedup of three orders of magnitude overthe commercial RTL energy estimator. Our experiments show thatthe proposed methodology also achieves good relative accuracy,which is essential in energy optimization studies. Hence, our

technique is both efficientand accurate.

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Outline

Whats the problem Introduction & related work

Extensible processor energy macromodelrequirements Proposed energy estimation methodology Experimental results and evaluation Conclusions

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Whats the Problem

Existing processor energy estimation frameworkis impractical for use in energy optimizationdone in the ASIP design cycle The extension to the base processor ISA is not fixed The number of configurations/extensions is large

Its essential to have a fast and accurate energyestimation of an application running on anextensible processor for each candidate

configuration in energy optimization studies

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Related Work

Structural macromodeling Characterize energy consumption of its constituent

hardware moduleE =Em1,i(bit transition) +Em2,i(bit transition) + +Emk,i(bit transition)( Em1,i(bit transition) denote energy per access of the module1)

z Advantage: High accuracyz Disadvantage:

1) Low efficiency (RTL simulation of a processor is extremely slow)2) Require RTL hardware description of the processor

Suitable for energy estimation of a processor core

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Related Work (cont.)

Instruction-level macromodeling Characterize energy consumption of each instruction of

the processor

E = EIC1* CycIC1+ EIC2 * CycIC2+ EIC3* CycIC3+.+ EICk* CycICk(EIC1denote average energy consumption by instruction class1 )(CycIC1denote number of cycles taken by instruction class1 )

z Energy coefficient EIC1

is acquired by actual measurementof a chipimplementation Advantage: High efficiency (Use ISS to yield energy estimation) Disadvantage:

1) Low accuracy

2) Require actual chip implement and this is infeasible forpower tradeoff studies early in the design cycle

Suitable for energy estimation of software on a fixed processorarchitecture

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Related Work (cont.)

Statistical analysis and prediction macromodeling Energy coefficients are calculated with regression analysis

to build the macromodel

Ei = C1 * M1,i+ C2 * M2,i+ .+ Ck* Mk,i+i ( i=1,2.n)(Total energy consumption Ei denote dependent variable)(Macromodel parameters M1,i. Mk,I denoteindependent variable)(i denote inaccuracy)

z Use a set of given (Ei, M1,i ,.,Mk,i) ,i=1,2n to predict the bestenergy coefficient C1 , C2 ,..,Ck

Energy macromodel generation

=1 * M1+ 2 * M2,+ .+k * Mk(1,..,k denotethe estimate ofenergy coefficient)( denotes the estimate of total energy consumption )(Macromodel parameters M1,..,Mk are observable during ISS )

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Paper Overview and Contributions

Hybrid energy macromodeling Instruction-level macromodeling for base processor Structural macromodeling for custom hardware extension Regression macromodeling for energy characterization

Contributions Energy consumption can simply be determined by instruction set

simulation Combines the efficiency ofinstruction-level approaches and the

accuracy ofstructural approaches Only needs the custom instruction descriptions Doest require the custom processor to be synthesized This is the only work on evaluate energy/performance tradeoff

among candidate custom instructions for extensible processor at

the early design cycle

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Extensible Xtensa Processor

Xtensas ISA consists of a basic set of instructions plus aset of configurable and extensible options

Extensibility is achieved by specifying application-specific

functionality through custom instructions The behavior of the custom instruction is descried using TIE

(Tensilica Instruction Extension) language TIE is independent of the processors pipeline

z Only need to describe the semantics of the instructions as ifthey consist of only combination logic

The TIE compiler automatically derives

The hardware implementation of custom instructions Corresponding software development kit for the configuration

z ANCI C/C++ compiler, linker, assembler, debuggerz Cycle-accurate instruction set simulator (ISS)

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Example Containing Three Custom Instructions

user register statement Specify the custom state register

and indices

iclass statement Define a new instruction class

with one or multiple custominstructions

semantic statement Describe the behavior of theinstruction class

schedule statement

(Used for multiple cycle instruction) Schedule the operation

sequence of the custominstruction Need ars and art at the beginning of first cycle

Need ACCU at the beginning of second cycle

Produce new ACCU at the end of second cycle

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Partial Architecture of an Extended Processor

Augmented with custom hardware to implement three custominstruction: MULT, MAC and CUS

MULT and MAC perform their functionality using shared customhardware (which is dependent ofbase processor operand buses) A multiplier (X), a multiplexer (MUX1), and an adder (+1)

CUS accesses custom register CR0CR2 (which is independent ofbase processor operand buses)

temp1 temp2

ACCU

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Snapshot of Dynamic Execution of a Program

Top horizontal bar lists the sequence of processor events dictated byits execution

The bottom bar depicts the side effects in either the base processor orthe custom hardware Execution of the base processor instruction add actives custom hardware (X, MUX1,+1) in the second cycle Execution of the custom instructions (I2 and I3) active base processor hardware

(ALU) in the second cycle Side effect occurs because the custom hardware and the ALU of the base

processor share the same operand buses

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Different Factors of the Energy Macromodel

Energy consumed by base processor instructions on the baseprocessor core Energy dependency on inter-instruction correlation and other

nonideal features (such as stalls, cache misses, etc.) Energy consumed by custom instructions on the custom

hardwarez Only custom hardware computation energy

The second box in the top bar of I2, I3, I4

Interplay between the base processor and custom hardware Active energy ofcustom hardware owing to base processor instructions

z Computation side effect in the EXE stage The bottom bar of instruction I1

Active energy ofbase processor hardware owing to custom instructionsz Computation side effect in the EXE stage

The bottom bar of instructions I2 and I3z Involvement of the base processor in other pipeline stages

RdReg, Wait, WrReg, WrCR event in the top bar of instruction I2, I3, I4

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Extensible Processor Energy Estimation Flowchart

constructing macromodel templateE=E0X0+E1X1+ +EnXn

express energy consumption (dependent variable)as a function of those characteristic parameter

(independent variable)E0,..,En are constants called energy coefficientX1,...,Xn are chosen from both instruction-leveland structural domain

Test program suite incorporates

custom instructions to cover all thecustom HW library components

Regression analysis require knowledgeof both the dependent variable and the

independent variableStep 3-7 repeat for all the test programdependent variable

independent variable

Regression analysis finds the estimate ofenergy coefficient

(energy macromodel construction complete

Characterization Flow

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Extensible Processor Energy Estimation Flowchart

Step 9 gathers instruction-levelmacromodel parameter valuesinstruction-level execution statistics

Step 10 gathers structural macromodelparameter values

The activation of custom hardware

Estimation Flow

parameter values are fed to the energy

macromodel to yield the energy estimatio

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Energy Macromodel Template Generation

- Eins is a linear function of instruction-level parameters depicts energy on the base processor- Estruc is a linear function of structural parameters depicts energy on custom hardware

Instruction-level macromodel parameters

Reflect the usage ofbase processor core due to either base processor orcustom instructions

Energy components of the base processor core Energy ofbase processor owing to base processor instructions

z Earith,.., Ebr_utk represent the average energy consumption ofeach instruction classz Cycarith,.., Cycbr_utk represent the number of cycles taken by each instruction class

Energy due to inter-instruction correlation and other nonideal featuresz Macromodel parameters Numi,..,Numinterlock denote the number of times each

nonideal case occurs

Energy consumption in the base processor imposed by custom instructions(Energy consumption in the four pipeline stages other than the EXE stage)

z Macromodel parameter Cycside_tie accounts for the number of cycles taken by allcustom instructions

Eins= Earith*Cycarith + Eld*Cycld + Est*Cycst + Ej*Cycj + Ebr_tk* Cycbr_tk + Ebr_utk*Cycbr_utk +

Ei*Numi + Ed*Numd + Euncache* Numuncache + Einterlock*Numinterlock +Eside_tie*Cycside_tie

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Energy Macromodel Template Generation

Structural macromodel parameters Reflect the usage ofcustom hardware extensions due to either base processor

or custom instructions

z Macromodel parameters Cyc1,,Cyc10denote the number of cycles in whicheach custom hardware component category is active

z Energy coefficients E1,..,E10 represent the average energy consumption for eachkind ofcustom hardware component category

Energy components of the custom hardware extensions Custom functional blocks is activated when any custom instructions executing Custom functional blocks can also be activated when base processor

instructions are runningz Side effect due to the sharing of the same operand buses still affects the custom

hardware

Dynamic resource usage analysis in the execution trace identifies the activated

custom functional blocks (HW component) for each instruction

Custom hardware energy consumption expresses as below:Estruc= E1 * Cyc1 + E2 * Cyc2 + E3 * Cyc3 +.+E10 * Cyc10Note: structural macromodel parameters should be covered all the components present in

the custom hardware library (10 component categories is this paper)

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Macromodel Fitting Through Regression Analysis

Determining the energy coefficients in the macromodel template Solving the linear-matrix equation M(n*21) X C(21*1)=E(n*1)

E denotes a n*1 column vector which are grouped by the

energy consumption data of n test programs M denotes a n*21 matrix which are grouped by the values

corresponding to the macromodel parameters C is the energy coefficient vector corresponding to

{Earith, Eld, Est, Ej, Ebr_tk, Ebr_utk, Ei, Ed, Euncache, Einterlock, Eside_tie, E1, E2,E3, E4, E5, E6, E7, E8, E9, E10 }

( denotes the estimate ofenergy coefficient C)

( denotes the estimate of total energyconsumption E)Yields the energy coefficient vector C, such thatthe mean square error is minimized

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Energy Coefficients of the Xtensa Processor

Energy consumption foreach base processor

instruction category percycle

Energy consumption forside-effectper cycle

Energy consumption forexecution-time effects permiss/per-interlock

Energy consumption for

different custom hardwarecomponents per cycle

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Absolute Accuracy Examination

Application Energy Estimates

The maximumestimation error is 8.5%

The average absolute error is only 3.3% The proposed energy estimation methodology is very fast WattWatcher needs several more hours for energy estimation

( RTL description generation +RTL simulation +power estimation using

WattWatcher )

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Absolute Accuracy Examination (cont.)

Energy consumption due to custom hardware can be significant

The accuracy of the macromodel is high both for the baseprocessor and custom hardware

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Relative Accuracy Examination

Good relative accuracy of our macromodel

The proposed energy estimation methodology is highrelative accuracy and low effort (no custom processorgeneration, no RTL simulation)

Therefore, it is highly suitable for energy optimization studies

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Conclusions

Presented an efficient and accurate energy estimationmethodology for extensible processors High efficiency comes from energy estimation only requires

instruction-set simulation based analysis of the application High accuracy comes from dynamic analysis ofcustom

hardware usage pattern

Although it speedup energy estimation, but it still havegood absolute accuracy (average absolute error is only 3.3%)and also achieve high relative accuracy

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