1. CS294-6 Reconfigurable Computing Day 5 September 8, 1998 Comparing Computing Devices

2. Quotes An engineer is a man who can do for a dime what any fool can do for a dollar. If it can’t be expressed in figures, it is not science; it is opinion. -- Lazarus Long

3. Motivation Need to understand –How costly (big) is a solution –How compare to alternatives –Cost and benefit of flexbility

4. What we really want: Complete implementation of our application For each architectural alternatives –In same implementation technology –w/ multiple area-time points

5. Reality Seldom get it packaged that nicely –much work to do so – technology keeps moving Deal with –estimation from components –technology differences –few area-time points

6. Today Empirical Start sorting out –custom vs. configurable –spatial configurable vs. temporal

7. FPGA Table

8. How many gates?

9. “gates” in 2-LUT

10. Now how many?

11. Gates/unit area? Usable gates?

12. Gates Required? Depth=3, Depth=2048?

13. Gate metric for FPGAs? Day3: several components for computations –compute element –interconnect: space time –instructions Not all applications need in same balance Assigning a single “capacity” number to device is an oversimplification

14. Exercise Admin Simulation slow -> see fastsim alternative Exercise 1 more effort than anticipated Drop exercise 4 and rearrange due date –SPACE1 --- ASAP –SPACE2 --- original 9/10, try before 9/15 –CYCLE --- 9/24

15. Density vs. Binding Time Density Binding Time Full Custom Gate Array FPGA Processor Pre-mask Final Mask(s) cycle “startup”

16. MPGA vs. Custom? AMI CICC’83 –MPGA 1.0 –Std-Cell 0.7 –Custom 0.5 Toshiba DSP –Custom 0.3 Mosaid RAM –Custom 0.2 GE CICC’86 –MPGA 1.0 –Std-Cell 0.4--0.7 FF/counter 0.7 FullAdder 0.4 RAM 0.2

17. Metal Programmable Gate Arrays

18. MPGAs Modern -- “Sea of Gates” yield 35--70% maybe 5k  /gate ? (quite a bit of variance)

19. MPGA vs. FPGA MPGA (SOG GA) –5K 2 /gate –35-70% usable (50%) –7-17K 2 /gate net Ratio: 2--10 (5) Xilinx XC4K –1.25M 2 /CLB –17--48 gates (26?) –26-73K 2 /gate net Adding ~2x Custom/MPGA, Custom/FPGA ~10x

20. MPGA vs. FPGA MPGA (SOG GA) –  –  gd ~1ns Ratio: 1--7 (2.5) Xilinx XC4K –  –  gates in 7ns –2-3 gates typical

21. Processors and FPGAs

23. Degrade from Peak: Processors Ops w/ no gate evaluations (interconnect) Ops use limited word width Stalls waiting for retimed data

24. Degrade from Peak: FPGAs Long path length --> not run at cycle Limited throughput requirement –bottlenecks elsewhere limit throughput req. Insufficient interconnect Insufficient retiming resources (bandwidth)

25. Degrade from Peak: Custom/MPGA Solve more general problem than required –(more gates than really need) Long path length Limited throughput requirement Not needed or applicable to a problem

26. Raw Density Summary Area –MPGA 2-3x Custom –FPGA 5x MPGA Area-Time –Gate Array 6-10x Custom –FPGA 15-20x Gate Array –Processor 10x FPGA

27. Raw Density Caveats Processor/FPGA may solve more specialized problem Problems have different resource balance requirements –…can lead to low yield of raw density

28. Broadenning Picture Compare larger computations For comparison –throughput density metric: results/area-time normalize out area-time point selection high throughput density –-> most in fixed area –-> least area to satisfy fixed throughput target

29. Multiply

30. FIR

31. IIR/Biquad

32. DES Keysearch

33. DNA Sequence Match Problem: “cost” of transform S 1  S 2 Given: cost of insertion, deletion, substitution Relevance: similarity of DNA sequences –evolutionary similarity –structure predict function Typically: new sequence compared to large databse

34. DNA Sequence Match

35. Floating-Point Add (single prec.)

36. Floating-Point Mpy (single prec.)

37. Summary Raw densities (Area-Time) –FPGA/custom = 100x –Processor/custom = 1000x Special-purpose functional units in processors/DSPs, much lower net benefit since need to control and interconnect Gap narrows (closes) as programmable can be specialized