Power Estimation and Modeling

1. Power Estimation and Modeling M. Balakrishnan CSV881: Low Power Design

2. Outline • Estimation problem • Estimation at different levels • System level • Algorithmic level • Processor level • RT level • Gate level • Circuit level CSV881: Low Power Design

3. Power Estimation Problem The objective in Power estimation is similar to other estimation problems; one tries to minimize time for estimation to achieve a certain accuracy or maximize accuracy for a given effort. Hi-fidelity is another objective CSV881: Low Power Design

4. Abstraction Levels • Higher the level of abstraction, it is likely to take less time but also produce lower accuracy • Suitable models to speedup estimation at higher levels • Primitive operations or structures keep on changing as we move up the abstraction levels CSV881: Low Power Design

5. System Level • Energy estimation in terms of very coarse granularity events e.g. specific tasks initiated by specific triggers or interrupts • Estimates for components like memory, buses etc. handled separately • Support for system level power management decisions CSV881: Low Power Design

6. System Level Approaches • Early approaches [3] were based on Monte-Carlo simulation. • Random input vectors were generated and power data for them generated using simulation • Approaches varied in terms of efficiency and accuracy. Some approaches provided for confidence level to be controlled • Quality of results depend on the “statistical” properties of the input vectors and their impact on power • Difficult to handle various power modes of operation • A recent approach for IP power estimation [4] works with hierarchical models CSV881: Low Power Design

7. Park et al [4] • Estimation at different hierarchical levels • Direct tradeoff between accuracy and time for estimation • Creation of power models at different levels • TLM (Transaction level modeling) CSV881: Low Power Design

8. Park et al[4] contd H.264 Prediction IP Active IDLE Inter Intra Chroma Chroma Luma (4X4) Luma Luma (16X16) Loc 0 Loc m Mod 0 Mod n CSV881: Low Power Design

9. Algorithmic/Behavioral Level • Models of RT level components expressed in terms of input characteristics that can be extracted from the behavior • e.g. adder operation energy in terms of word-length and hamming distance of inputs • e.g. memory energy per read or write access • Energy estimation based on weighted sum of such basic operations • Behavioral transformations to be supported in terms of energy change • Prediction of interconnect power consumption to support data transfer is an issue CSV881: Low Power Design

10. Algorithmic Energy Components Total energy consumed = energy consumed in computation + energy consumed in storage access + energy consumed in data transfer + energy consumed in control (function of allocation as well as binding) CSV881: Low Power Design

11. Adder Module Characteristics E n e r g y Hamming distance CSV881: Low Power Design

12. Processor Level • First proposed by Tiwari et. al [5,6] for software power estimation • The methodology is based on measuring power consumption for each instruction and • Overall energy consumption is computed by taking a weighted sum of number of instructions of each type. The weighting factor is the power consumption of the individual instructions • This approach based on measurements is valid only for a processor which has been fabricated. Sama et.al [7] have modified it to create an instruction level power model with a gate level simulator CSV881: Low Power Design

13. Vivel Tiwari’s Model[5] Energy cost of an instruction = base cost (measuring current on a repetitive set of identical instructions) + circuit state overhead cost (measuring current on pairs of instruction) + resource constraint cost (to account for stall cycles due to resource contention) + cache energy costs (to account for cache misses) Tiwari observed that at least for CISC processors, operand and data value variations affect less than 3% of the total energy consumption. CSV881: Low Power Design

14. Lee et al [6] • Approach similar to the one proposed in [5]. Processor used is Fujitsu DSP processor instead of DX486 (Intel processor) • Base cost of the instructions varies significantly unlike CISC processor • Instructions classified into 6 different classes to reduce the size of measurements. (individual as well as pair wise measurements) • Power minimization strategies suggested include • “Intelligent” register bank assignment • Instruction packing to reduce cycles • Instruction scheduling to reduce circuit state switching energy • Operand swapping to reduce computation in Booth’s algorithm CSV881: Low Power Design

15. Sama et al[7] • Instruction set model similar to the models proposed by Tiwari[5] • Energy numbers obtained through a power simulator rather than actual measurement; thus models possible at design time and can be part of micro-architecture and/or instruction set architecture exploration • Considerable speedup over gate-level or circuit-level simulation of the processor model CSV881: Low Power Design

16. Issues in Instruction Set Power Models • Instructions are not executed one at a time • All current processors are deeply pipelined and as many instructions are active concurrently in the pipeline, their interactions should also be accounted for • Tiwari[5] also measured interactions between consecutive instructions from different classes • The effect of varying data (as well as address) is ignored in the model • Though can be accounted by an additive factor CSV881: Low Power Design

17. RT Level Estimation • Models of RTL components like adders, comparators, decoders, multiplexers etc. • Models based on effective capacitance • Switching activity estimated from the RTL code CSV881: Low Power Design

18. Gate Level Estimation • Effective capacitance models at the gate level in the library • Switching activity is estimated for a given application (specified as a set of Boolean equations) • Switching activity could be based on probabilistic input vector characteristics or actual input vector characteristics CSV881: Low Power Design

19. Circuit Level • Comparison is always with SPICE • How much faster and how close in terms of prediction? • Compact set of vectors • How representative are these vectors in terms of actual use? • Powermill [3] a popular tool achieves 2 to 3 order speedup while being within 10% accuracy. This is based on event driven timing simulation and uses a simplified table –driven device models. CSV881: Low Power Design

20. References • M. Pedram, “Power Minimization in IC Design”, ACM TODAES, Vol. 1., No. 1, Jan. 1996, pp. 3-56 • Macii et al, “High-level Power Modeling, Estimation and Optimization”, DAC 1997 • Burch et al, “ A Monte-carlo Approach for Power Estimation”, IEEE TVLSI, Vol. 1, No. 1, Mar. 1993. pp. 63-71 • Park et al, “System Level Power Estimation Methodology with H.264 Decoder Prediction IP Case Study”, pp. 601-608 CSV881: Low Power Design

21. References (contd) • Tiwari et al, “Power Analysis of Embedded Software: A First Step towards Software Power Minimization”, IEEE TVLSI, Vol. 2, No. 4, Dec. 1994, pp. 437-444 • Lee et al, “Power Analysis and Minimization Techniques for Embedded DSP Software ”, IEEE TVLSI, Vol. 5, No. 1, Mar 1997, pp. 123-135 • Sama et al, “Speeding up Power Estimation of Embedded Software”, ISLPED 2000, pp. 191-196 CSV881: Low Power Design