Automatic Insertion of Low Power Annotations in RTL for Pipelined Microprocessors

1. Automatic Insertion of Low Power Annotations in RTL for Pipelined Microprocessors Vinod Viswanath The University of Texas at Austin

2. Outline • Power Dissipation in Hardware Circuits • Instruction-driven Slicing to attain lower power dissipation • Automatically annotates microprocessor description • At the Register Transfer Level and Architectural level • Applying Instruction-driven Slicing to pipelined architectures • Applying Instruction-driven Slicing to out-of-order superscalar architectures

3. Power Dissipation • Switching activity power dissipation • To charge and discharge nodes • Short Circuit power dissipation • High only for output drivers, clock buffers • Static power dissipation • Due to leakage current

4. Switching Activity Power Dissipation • Transistor-level • Reordering, sizing • Gate-level • Don’t-care optimizations (combinational) • Encoding (sequential) • Pre-computation based optimization (sequential) • Guarded evaluation (sequential) • RT-level • Use program structure and dataflow information available at that level of abstraction

5. Instruction-driven Slice • An instruction-driven slice of a microprocessor design is • all the relevant circuitry of the design required to completely execute a specific instruction • Parts of the decode, execute, writeback etc. blocks • Cone of influence of the semantics of the instruction

6. Instruction-driven Slicing • Given a microprocessor design and an instruction • Identify the instruction-driven slice • Shut off the rest of the circuitry • This might include • Gating out parts of different blocks • Gating out floating point units during integer ALU execution • Turning off certain FSMs in different control blocks since exact constraints on their inputs are available due to instruction-driven slicing

7. Algorithm (High Level) • Algorithm instruction-driven-slicing. Begin • Inputs: vRTL (Verilog RTL), insts (instructions) • Output: aRTL (Annotated RTL) • Parse vRTL to obtain the Abstract Syntax Program Graph (ASPG) • For each instruction I in insts repeat • Slice the ASPG for instruction I • Traverse the ASPG • Add annotation variables if such a block is found • If a particular flop is already gated, then add the current annotation in an optimal fashion • Return the annotated ASPG • Generate Verilog code (aRTL) for the annotated ASPG End.

8. or1200_ctrl.lsu_op

9. Methodology • In order to demonstrate our technique • We have incorporated instruction-driven slicing as part of the traditional design flow • The vRTL model is annotated to obtain the aRTL model • Synopsys Design Environment has been sufficiently modified to accept the aRTL, SPEC2000 benchmarks and power process parameters and estimate the power dissipation due to switching activity • The annotated Architectural model is fed to the SimpleScalar simulator with the Wattch power estimator to estimate the power dissipation

10. Methodology

11. Experiment: OR1200 • We have used our tool-chain to test our methodology on OR1200 • OR1200 is a single-instruction-issue pipelined microprocessor implementing the OpenRISC ISA. • 4-stage integer pipeline with single instruction issue per cycle • We have annotated both the RTL and the architectural models of OR1200

12. Experiment: OR1200

13. OR1200-RTL Results • Results are shown after annotation insertion • Sliced on 1, 4, 10 instructions • For SPECINT2000 benchmarks • Power dissipation decreases consistently

14. OR1200-Arch Results • Results are shown after annotation insertion • Sliced on 1, 4, 10 instructions • For SPECINT2000 benchmarks • Power dissipation decreases consistently

15. OR1200 Results (contd.) • Power gains are consistently good • Power gains far outperform area losses

16. OR1200 Results (contd.) • Flop distribution shown before slicing (Fig. a) after slicing on add, l.add (Fig. b) and after slicing on load, l.lw (Fig. c) Fig. a Fig. b Fig. c

17. Experiment: PUMA • We have used our tool-chain to test our methodology on PUMA • PUMA is a dual-issue, out-of-order super-scalar, fixed-point PowerPC core • We have annotated both the RTL and the architectural models of PUMA

18. PUMA Results (contd.) • Power gains are good upon slicing for a few instructions (~7) before delay losses start dominating (Fig. 1) • Power gains far outperform area losses (Fig 2) • Flop distribution shown before slicing (Fig. 3a) after slicing on add (Fig. 3b) and after slicing on load (Fig. 3c) Fig.3a (Fig. 1) Fig.3b (Fig. 2) Fig.3c

19. Conclusions • Proposed Instruction-driven Slicing as a new technique to automatically reduce power dissipation • Implemented the methodology of incorporating instruction-driven slicing into the design flow tool-chain • Inserting these annotations preserves the functionality of the circuit

20. Conclusions (continued) • This technique seems most applicable to single-issue multi-staged pipelined machines. • When there are multiple instructions in-flight in the same pipeline stage, the gains of a single-instruction-abstraction are lost. • Graphics processors, various embedded applications are more often better suited for this technique than general purpose out-of-order superscalars.

21. Spare slides

22. PUMA: a fixed point PowerPC core

23. PUMA Power Gain Results • Results are shown after annotating the • RTL (left) and Architectural (Right) models • For un-sliced and sliced on 1, 4, 10 instructions • For SPECINT2000 benchmarks • Power dissipation decreases consistently

24. Comparing OR1200 and PUMA

25. Correct Annotations • Notion of correctness • Original RTL and the annotated RTL should be functionally equivalent under all conditions • Correctness theorem (defthm or1200_slicing_correct (equal (or1200_cpu n) (or1200_cpu_sliced n)))

26. ACL2 Theorem Prover • First order logic general purpose theorem prover • Breakdown the theorem into sub-goals • Many engines work on the sub-goals and will either prove them or break them down further and add to the central pool of goals to be proved • Success story in Hardware • Verified FDIV in the AMD processors

27. Proof Methodology

28. Proof Methodology • The RTL is a shallow embedding in ACL2 • Convert Verilog RTL into ACL2RTL • We have created a large RTL library to recognize as well as analyze ACL2RTL • Slicing is done on the Verilog code • Both original and annotated Verilog are converted into ACL2 and we construct the functional equivalence proof in ACL2

29. Verilog to ACL2

30. Proof Structure • Create a library of functions to interpret the ACL2 model of the RTL • Functional equivalence theorem is built up block by block