A Hybrid Energy-Estimation Technique for Extensible Processors

1. A Hybrid Energy-Estimation Technique for Extensible Processors Fei, Y.; Ravi, S.; Raghunathan, A.; Jha, N.K.IEEE Transactions onComputer-Aided Design of Integrated Circuits and Systems Volume: 23  Issue: 5 Pages: 652-664 May 2004

2. Abstract • In this paper, we present an efficient and accurate methodology for estimating the energy consumption of application programs running on extensible processors. Extensible processors, which are getting increasingly popular in embedded system design, allow a designer to customize a base processor core through instruction set extensions. Existing processor energy macromodeling techniques are not applicable to extensible processor, since they assume that the instruction set architecture as well as the underlying structural description of the micro-architecture remain fixed. Our solution to the above problem is a hybrid energy macromodel suitably parameterized to estimate the energy consumption of an application running on the corresponding application-specific extended processor instance, which incorporates any custom instruction extension. Such a characterization is facilitated by careful selection of macromodel parameters/variables that can capture both the functional and structural aspects of the execution of a program on an extensible processor.

3. Abstract (cont.) • Another feature of the proposed energy characterization flow is the use of regression analysis to build the macromodel. Regression analysis allows for in-situ characterization, thus allowing arbitrary test programs to be used during macromodel construction. We validated the proposed methodology by characterizing the energy consumption of a state-of-the-art extensible processor (Tensilica’s Xtensa). We used the macromodel to analyze the energy consumption of several benchmark applications with custom instructions. The mean absolute error in the macromodel estimates is only 3.3%, when compared to the energy values obtained by a commercial tool operating on the synthesized register-transfer level (RTL) description of the custom processor. Our approach achieves an average speedup of three orders of magnitude over the commercial RTL energy estimator. Our experiments show that the proposed methodology also achieves good relative accuracy, which is essential in energy optimization studies. Hence, our technique is both efficient and accurate.

4. Outline • What’s the problem • Introduction & related work • Extensible processor energy macromodel requirements • Proposed energy estimation methodology • Experimental results and evaluation • Conclusions

5. What’s the Problem • Existing processor energy estimation framework is impractical for use in energy optimization done in the ASIP design cycle • The extension to the base processor ISA is not fixed • The number of configurations/extensions is large • It’sessentialto havea fast and accurate energy estimation of an application running on an extensible processor for each candidate configuration in energy optimization studies

6. Related Work • Structural macromodeling • Characterize energy consumption of it’s constituent hardware module E =∑Em1,i(bit transition) + ∑Em2,i(bit transition) + …… + ∑Emk,i(bit transition) ( Em1,i(bit transition)denote energy per access of the module1) • Advantage: High accuracy • 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

7. 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 ) • Energy coefficient EIC1is acquired by actualmeasurement of a chip implementation • Advantage: High efficiency (Use ISS to yield energy estimation) • Disadvantage: 1) Low accuracy 2) Require actual chip implement and this is infeasible for power tradeoff studies early in the design cycle • Suitable for energy estimation of software on a fixed processor architecture

8. 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 Eidenotedependent variable) (Macromodel parameters M1,i…. Mk,I denoteindependent variable) (∆i denote inaccuracy) • Use a set of given (Ei,M1,i ,….,Mk,i),i=1,2…n to predict the best energy coefficient C1 , C2 ,..,Ck • Energy macromodel generation Ê = Ĉ1 * M1+ Ĉ2 * M2,+ …….+ Ĉk * Mk (Ĉ1,..,Ĉkdenotethe estimate of energy coefficient) (Êdenotes the estimate of total energy consumption ) (Macromodel parametersM1,..,Mk are observable during ISS )

9. 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 of instruction-level approaches and the accuracy of structural approaches • Only needs the custom instruction descriptions • Does’t 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

10. Extensible Xtensa Processor • Xtensa’s ISA consists of a basic set of instructions plus a set 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 processor’s pipeline • Only need to describe the semantics of the instructions as if they consist of only combination logic • The TIE compiler automatically derives • The hardware implementation of custom instructions • Corresponding software development kit for the configuration • ANCI C/C++ compiler, linker, assembler, debugger • Cycle-accurate instruction set simulator (ISS)

11. 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 custom instructions • semantic statement • Describe the behavior of the instruction class • schedule statement (Used for multiple cycle instruction) • Schedule the operation sequence of the custom instruction • 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

12. Partial Architecture of an Extended Processor • Augmented with custom hardware to implement three custom instruction: MULT, MAC and CUS • MULT and MAC perform their functionality using shared custom hardware (which is dependent of base processor operand buses) • A multiplier (X), a multiplexer (MUX1), and an adder (+1) • CUS accesses custom register CR0…CR2 (which is independent of base processor operand buses) temp1 temp2 ACCU

13. Snapshot of Dynamic Execution of a Program • Top horizontal bar lists the sequence of processor events dictated by its execution • The bottom bar depicts the side effects in either the base processor or the 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

14. Different Factors of the Energy Macromodel • Energy consumed by base processor instructions on the base processor core • Energy dependencyon inter-instruction correlation andother nonideal features(such asstalls, cache misses, etc.) • Energy consumed by custom instructions on the custom hardware • 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 of custom hardware owing to base processor instructions • Computation side effect in the EXE stage • The bottom bar of instruction I1 • Active energy of base processor hardware owing to custom instructions • Computation side effect in the EXE stage • The bottom bar of instructions I2 and I3 • Involvement of the base processor in other pipeline stages • RdReg, Wait, WrReg, WrCR event in the top bar of instruction I2, I3, I4

15. Extensible Processor Energy Estimation Flowchart Characterization Flow • constructing macromodel template E=E0X0+E1X1+ …+EnXn • express energy consumption (dependent variable) • as a function of those characteristic parameter • (independent variable) • E0,..,En are constants called energy coefficient • X1,...,Xn are chosen from both instruction-level • and structural domain • Test program suite incorporates • custom instructions to cover all the • custom HW library components • Regression analysis require knowledge • of both the dependent variable and the • independent variable • Step 3-7 repeat for all the test program dependent variable independent variable • Regression analysis finds the estimate of energy coefficient (energy macromodel construction complete)

16. Extensible Processor Energy Estimation Flowchart Estimation Flow • Step 9 gathers instruction-level macromodel parameter values • instruction-level execution statistics • Step 10 gathers structural macromodel parameter values • The activation of custom hardware • parameter values are fed to the energy macromodel to yield the energy estimation

17. 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 of base processor core due to either base processor or custom instructions • Energy components of the base processor core • Energy of base processor owing to base processor instructions • Earith,.., Ebr_utk represent the average energy consumption of each instruction class • Cycarith,.., Cycbr_utk represent the number of cycles taken by each instruction class • Energy due to inter-instruction correlation and other nonideal features • Macromodel parameters Numi,..,Numinterlock denote the number of times each nonideal case occurs • Energy consumption in thebase processorimposed bycustom instructions(Energy consumption in the four pipeline stagesother than the EXE stage) • Macromodel parameter Cycside_tie accounts for the number of cycles taken by all custom instructions Eins= Earith*Cycarith + Eld*Cycld + Est*Cycst + E j*Cyc j + Ebr_tk* Cycbr_tk + Ebr_utk*Cycbr_utk + Ei*Numi + Ed*Numd + Euncache* Numuncache + Einterlock*Numinterlock + Eside_tie*Cycside_tie

18. Energy Macromodel Template Generation • Structural macromodel parameters • Reflect the usage of custom hardware extensions due to either base processor or custom instructions • Macromodel parameters Cyc1,…,Cyc10 denote the number of cycles in which each custom hardware component category is active • Energy coefficients E1,..,E10 representthe average energy consumption for each kind of custom 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 running • Side effect due to the sharing of the same operand buses still affects the custom hardware • Dynamic resource usage analysisin the execution trace identifies theactivated custom functional blocks (HW component)for each instruction Custom hardware energy consumption expresses as below: Estruc= E1 * Cyc1 + E2 * Cyc2 + E3 * Cyc3 +….+E10 * Cyc10 Note: structural macromodel parameters should be covered all the components present in the custom hardware library (10 component categories is this paper)

19. Macromodel Fitting Through Regression Analysis • Determining the energy coefficients in the macromodel template • Solving the linear-matrix equationM(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} • ( Ĉ denotesthe estimate of energy coefficient C) • ( Ê denotes the estimate of total energy • consumptionE) • Yields the energy coefficient vector C, such that • the mean square error is minimized

20. Energy Coefficients of the Xtensa Processor • Energy consumption for each base processor instruction category per cycle • Energy consumption for side-effect per cycle • Energy consumption for execution-time effects per miss/per-interlock • Energy consumption for different custom hardware components per cycle

21. Absolute Accuracy Examination Application Energy Estimates • The maximum estimation 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 )

22. Absolute Accuracy Examination (cont.) • Energy consumption due to custom hardware can be significant • The accuracy of the macromodel is high both for the base processor and custom hardware

23. Relative Accuracy Examination • Good relative accuracy of our macromodel • The proposed energy estimation methodology is high relative accuracy and low effort(no custom processor generation, no RTL simulation) • Therefore, it is highly suitable for energy optimization studies

24. Conclusions • Presented an efficient and accurate energy estimation methodology 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 of custom hardware usage pattern • Although it speedup energy estimation, but it still have good absolute accuracy(average absolute error is only 3.3%) and also achieve high relative accuracy