1. Parameterized Embedded Systems Platforms Frank Vahid Students: Tony Givargis, Roman Lysecky, Susan Cotterell Dept. of Computer Science and Engineering University of California, Riverside Member, Center for Embedded Computer Systems, UC Irvine The Dalton Project Supported by: NSF, NEC

2. 2 Outline Introduction Parameterized SOC platforms Exploring parameter configurations Future direction: self-optimizing platforms Conclusions

3. 3 IC Introduction Advent of system-on-a-chip Micro- proc. IC Memory IC Peripher. IC FPGA IC Board Microprocessor core (aka “IP”) Peripheral core Introduction

4. 4 System-on-a-chip (SOC) Introduction

5. 5 The Productivity Gap [ITRS99]

6. 6 Programmable Platforms (ITRS99) Pre-fabricated IC, synthesizable HDL, or both –“reference designs” (VLSI), “silicon platforms” (Philips), “fig chips” (Vahid/Givargis99) Micro- processor CacheMemoryDMABridge FPGA Peripheral System bus Peripheral bus Programmable Platform Introduction

7. 7 Targeted to Embedded Systems May drive future architecture design [Patterson98] Varied power/performance/size constraints –Programmable platforms must adapt Introduction

8. 8 Micro- processor CacheMemoryDMABridge FPGA Peripheral System bus Peripheral bus Programmable Platform Adapting platforms to constraints One solution: Architectural Parameters Application1 main() while (…) { … } Cache Application2 main() while(…) { … } Cache Introduction

9. 9 Related work Pleiades project [Rabaey97] VLSI’s Velocity ArchitectureApplications Numbers Mapping Analysis Our focus Introduction Microprocessor + FPGA Philips’ Y-Chart approach Microcontrollers

10. 10 Outline Introduction Parameterized SOC platforms Exploring parameter configurations Future direction: self-optimizing platforms Conclusions

11. 11 Basic parameters -- cache Micro- processor CacheMemoryDMABridge FPGA Peripheral System bus Peripheral bus Programmable Platform Cache Parameterized Systems-on-a-chip

12. 12 Basic parameters -- cache TagIndexOffset VTDVTD == Mux Data Associativity Cache Size Line Size Parameterized Systems-on-a-chip

13. 13 Micro- processor CacheMemoryDMABridge FPGA Peripheral Programmable Platform System bus Peripheral bus Basic parameters -- bus Parameterized Systems-on-a-chip

14. 14 Basic parameters -- Bus Bus Change Bus Width [Givargis98] C1C1 C2C2 C 1 > C 2 Parameterized Systems-on-a-chip Mux Demux Mux Demux Bus

15. 15 Basic parameters -- Bus Bus Parameterized Systems-on-a-chip Encode data to reduce switching (Bus Invert) [Stan95] Encoder Decoder Encoder Decoder invert_ctrl 0100101101001011 1001011010010110 Hamming Dist = 6 0110100101101001 1 0100101101001011 0 invert_ctrl Binary Encoding Bus-Invert Encoding Hamming Dist = 3

16. 16 Parameter definitions Parameter –An architectural feature that can be varied, with a small set of possible values, without changing the application’s essential functionality. Configuration –A selection of a particular value for every architecture parameter Static vs. dynamic parameter –Static: Value is set before fabricating the IC. –Dynamic: Value is set after fabricating the IC. Parameterized Systems-on-a-chip

17. 17 Potential tradeoffs experiment [ICCAD99] Parameterized Systems-on-a-chip Micro- processor MemoryDMABridge FPGA Peripheral System bus Peripheral bus I-cache D-cache ParametersPossible values I-cache Size32k,16k,8k,4k,2k,1k,512,256,128 Line8, 16, 32 Associativity2, 4, 8 D-cache Size32k,16k,8k,4k,2k,1k,512,256,128 Line8, 16, 32 Associativity2, 4, 8 Mp-c bus Data bus width4, 8, 16, 32 Data bus inverton or off Sys. bus Data bus width4, 8, 16, 32 Data bus inverton or off

18. 18 Potential tradeoffs experiment [ICCAD99] Cache: Dinero [Edler, Hill] ISS: [Tiwari96] Micro- processor CacheMemory C Program Bus simulator Instr. Set Simulator Cache Simulator Memory Simulator Power Total power Parameterized Systems-on-a-chip

19. 19 Potential tradeoffs experiment X-axis: execution time (sec) Y-axis: power (watt) Tradeoff between performance and power Computed power for all 45,568 configurations –For each of four C applications –Used microprocessor, cache, and bus simulators (1 wk CPU) Parameterized Systems-on-a-chip

20. 20 Potential tradeoffs experiment Narrower bus required a larger cache size Bus: 8-1/32-1 I: 32k, 8, 8 D: 16k, 8, 16.995 sec, 3.4 W, 30K Bus: 16-1/32-1 I: 16k, 8, 16 D: 32k, 8, 8.389 sec, 11.4 W, 21kG Bus: 32-1/32-0 I: 16k, 4, 4 D: 16k, 4, 4.086 sec, 43.6 W, 20kG Parameterized Systems-on-a-chip

21. 21 Potential tradeoffs experiment Performance varied by 11x Power varied by 13x Area varied by 1x Energy consumption varied by 2x Parameterized Systems-on-a-chip

22. 22 Potential tradeoffs experiment Bus: 8-1/4-0, I: 1k, 2, 4 D: 512, 2, 4 5 ms,.02 W, 18kG Bus: 16-1/32-1 I: 1k, 4, 4 D: 512, 4, 8 3 ms,.07 W, 17kG Bus: 32-1/32-1 I: 1k, 4, 4 D: 512, 4, 8 2 ms,.19 W, 15kG Parameterized Systems-on-a-chip

23. 23 Potential tradeoffs experiment Performance varied by 2.5x Power varied by 9.5x Area varied by 1x Energy consumption varied by 4x Parameterized Systems-on-a-chip

24. 24 Potential tradeoffs experiment How much variation in total system power and performance can we obtain just by varying the cache and bus parameters? –9 to 14x improvement in power/performance How interdependent are these two types of parameters? –fixing cache param. values, then selecting bus param. values results in non-optimal solutions Parameterized Systems-on-a-chip

25. 25 Many more parameters possible Some examples include: –Code compression (Henkel/Wolf) –Address bus encoding –Multiple levels of memory hierarchy –CPU parameters (e.g., voltage scale, DP width) –Peripheral core parameters (our current focus) –Fertile research area Can yield even larger tradeoffs if we: –Create parameter-aware compiler –Adapt OS? Parameterized Systems-on-a-chip

26. 26 Outline Introduction Parameterized SOC platforms Exploring parameter configurations Future direction: self-optimizing platforms Conclusions

27. 27 Exploring parameter configurations Low-level simulation –Gate-level simulation Far too slow, days per configuration –RT-level simulation Still slow, hours per configuration Our approach –System-level simulation Minutes per configuration –System-level trace simulation Seconds per configuration –System-level trace analysis Milliseconds per configuration

28. 28 Evaluation by gate-level simulation Exploring Parameter Configurations Micro- processor CacheMemoryDMABridge FPGA Peripheral Programmable Platform System bus Peripheral bus Capture each core in HDL, synthesize, simulate HDL synthesis HDL simulation Total power Reconfigure Hours (often tens) per configuration

29. 29 Evaluation by system-level simulation Exploring Parameter Configurations Micro- processor CacheMemoryDMABridge Peripheral Peripheral bus C Program Trace Generator Bus simulator Instr. Set Simulator Cache Simulator Memory Simulator Power Total power OO models DMA Simulator Bridge Simulator Peripheral Simulator Peripheral Simulator Power Minutes-per-configuration Contrast with hours-per-config. Reconfigure

30. 30 Evaluation by trace-simulation Exploring Parameter Configurations OO non-fct. models Note that the cache simulator is non-functional Same approach for others –Get traces from small # of system simulation Bus trace Bus trace simulator Instr. trace Simulator Memory trace Simulator Instr. trace C Program Trace Generator Cache trace Simulator Address trace DMA trace Simulator Bridge trace Simulator Peripheral trace Simulator Peripheral trace Simulator Instr. traces Power Total power Power Reconfigure Seconds-per-configuration

31. 31 System simulation vs. trace simulation Parameter evaluation System level model Execute Power System level model Execute Traces Power uPDMA UART Trace simulators uPDMA UART Parameter evaluation

32. 32 Evaluation by trace-analysis Exploring Parameter Configurations Equations Further speedup -- –statistically-characterize traces –Still only small # of system simulations Bus stats. Bus trace simulator Instr. trace analyzer Memory trace analyzer Instr. stats. C Program Trace Generator Cache trace analyzer Address stats. DMA trace analyzer Bridge trace analyzer Peripheral trace analyzer Peripheral trace analyzer Instr. stats. Power Total power Power Reconfigure Milliseconds-per-configuration

33. 33 Trace-analysis approach for cache Given a trace of memory refs Cache parameters Size (S) Line/block-size (L) Associativity (A) Compute # of misses (N) Size (S) # of misses (N) Exploring Parameter Configurations

34. 34 Trace-analysis approach for cache Exploring Parameter Configurations

35. 35 Trace-analysis approach for cache Capture improvements obtainable by: –changing line-size at small/large values of cache-size –changing associativity at small/large values of cache-size Exploring Parameter Configurations

36. 36 Trace-analysis approach for bus Exploring Parameter Configurations Items/second Bus width Num transfers per item Random data capacitance

37. 37 Trace-analysis approach for bus Bus equation: m items/second (denotes the traffic N on the bus) n bits/item k bit wide bus bus-invert encoding random data assumption Exploring Parameter Configurations

38. 38 Trace-analysis experiments Bus ABus B Peripheral 1 Peripheral Bus Bridge CPU I-Cache D-Cache Peripheral 2Peripheral n Memory Cache parameters – size: 128, 256, 512, 1k, 2k, 4k, 8k, 16k, 32k – assoc: 2, 4, 8 – line: 8, 16, 32 Bus Parameters – width: 4, 8, 16, 32 – code: binary/bus-invert Analyzed 45K sets exhaustively for each of 4 examples. Exploring Parameter Configurations

39. 39 Experiment Results Diesel application’s performance Blue (light-gray) is system-simulation-based Red (dark-gray) is trace-analysis-based 4% error 320x faster Exploring Parameter Configurations

40. 40 Experiment Results Diesel application’s energy consumption Blue (light-gray) is obtained using full simulation Red (dark-gray) is obtained using our equations 2% error 420x faster Exploring Parameter Configurations

41. 41 Experiment Results CKey application’s performance Blue (light-gray) is obtained using full simulation Red (dark-gray) is obtained using our equations 8% error 125x faster Exploring Parameter Configurations

42. 42 Experiment Results CKey application’s energy consumption Blue (light-gray) is obtained using full simulation Red (dark-gray) is obtained using our equations 3 % error 125x faster Exploring Parameter Configurations

43. 43 Experiment Results 125 - 400x speedup 1-18% absolute error (power & performance) 2% average power error Time (hours) Power Error (%) Exploring Parameter Configurations

44. 44 Techniques for general cores Earlier experiments were for uP/cache/bus System simulation for other cores (ISSS’00) –Isolate “instructions” in system-level model –Gate-level simulation per instruction –Back-annotate system-level model’s instructions –Similar to technique for microprocessors, but: Must consider “power modes”

45. 45 Trace approach for general cores System level model Execute Traces Power Trace simulators uPDMA UART Parameter evaluation Full trace Reset -- Quantize P 1,P 2,…,P 64 IDCT P 1,P 2,…,P 64 Quantize P 1,P 2,…,P 64 IDCT P 1,P 2,…,P 64 Reduced trace with characterized data Reset -- Quantize.80 IDCT.72 Quantize.93 IDCT.63 Reduced trace with instructions only Reset -- Quantize -- IDCT -- Quantize -- IDCT -- Reduced trace with instruction frequencies Reset *1 Quantize *2 IDCT *2

46. 46 Experiments with general cores: JPEG trace file size (Kb)CPU time for power evaluation (sec) pixel size (bits) ftrcrtrc_ cd rtrc _i gatesysftrcrtrc_ cd rtrc_ i 10323.60.529000048264.94.6 12393.60.533000049275.14.6 average speedup:6K12K62K67K gateftrcrtrc_cdrtrc_ipixel size (bits) mJ errormJerrormJerror 104204435%4517%49117% 125315697%5768%63219% average error:6%7.5%18%

47. 47 Experiments with general cores: UART

48. 48 Outline Introduction Parameterized SOC platforms Exploring parameter configurations Future direction: self-optimizing platforms Conclusions

49. 49 Future directions Earlier work –used software on workstation to explore parameter configurations “Self-optimizing” platform –Can we build the exploration ability into the platform itself? –Transparent to the user Ease of use, more accurate metrics, wider acceptance, –“Embedded CAD” Workstation Platform Exploration sw Configuration Workstation Platform Exploration ability Regular binary

50. 50 Conclusions Parameters can improve usefulness of programmable platforms –by adapting platform to particular application and to power/performance constraints Good tradeoff range even for basic parameters Fast and accurate evaluation seems possible Much work remains –More parameters –Better exploration –Self-optimizing platforms