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DSP in FPGA

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DSP in FPGA

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  1. DSP in FPGA

  2. Topics Considerations When not to use Floating Point Example FP: Adder Hardware Circuit Constant Cache Data-path with Constant Cache FFT Example Other Examples: Simulink Equalizer Routing Challenge Routing Resources: Altera vs. Xilinx Example: Matrix Multiplication Hypothesis and Rule’s of Thumb Results Paper Analysis • Signal Processing • FPGA Applications with DSP • DSP milestones • PDSP Architecture • PDSP vs FPGA • Example: FIR Filter • DSP on FPGA • State of the Art • Flexibility • Multi-Channel Friendly • Resources • DSP Slice • Multiplication Modes • IP Blocks • IP Block Example: FIR Filter

  3. Signal Processing • Transform or manipulate analog or digital signal. • Most frequent application: filtering. • DSP has replaced related traditional analog signal processing systems in many applications.

  4. FPGA’s Applications

  5. Milestones Cooley and Tukey 1965 PDSP 1970 Compute (fixed-point) “multiply-and-accumulate” in only one clock cycle • Efficient algorithm to compute the discrete Fourier Transform (DFT) Today PDSPs: Floating-point multipliers, barrel shifters, memory banks, zero-overhead interfaces to A/D and D/A Converters

  6. PDSP Architecture • Single-DSP implementations have insufficient processing power for today’s system’s complexity. • Multiple-chip systems: more costly, complex and higher power requirements. • Solution: FPGAs

  7. Managing Resources &Design Reliability

  8. FPGA vs. PDSPs PDSPs FPGA Implement MAC at higher cost. High-bandwithSP applications through multiple MAC cells on one chip. Algorithms: CORDIC, NTT or error-correction algorithms Dominate more front-end (sensor) applications FIR filters, CORDICalgorithms FFTs • RISC paradigm with MAC • Advantage: multistage pipeline architectures can achieve MAC rates limited only by speed of array multiplier. • Dominate applications that required complicated algorithms (e.g. several if-then-else constructs)

  9. FPGA Advantages • Ability to tailor the implementation to match system requirements. • Multiple-channel or high-speed system: take advantage of the parallelism within the device to maximize performance, • Control logic implemented in hardware

  10. Fir Filter Example

  11. FPGA

  12. State of the Art (Xilinx)

  13. Flexibility • How many MACsdo you need? • For example, in FIR Filter, FPGAs can meet various throughput requirement

  14. Multi-Channel Friendly • Parallelism enables efficient implementation of multi-channel into a single FPGA. • Many low sample rate channels can be multiplexed and processed at a higher rate.

  15. Resources • Challenge: How to make the best use of resources in most efficient manner?

  16. DSP48E1 Slice Flexibility • 2 DSP48E1 slices per tile • Column Structure to avoid routing delay • Pre-adder, 25x18 bit multiplier, accumulator • Pattern detect, logic operation, convergent/symmetric rounding • 638 MHz Fmax

  17. Multiplication Modes • Each DSP block in a Stratixdevice can implement: • Four 18x18-bit multiplications, • Eight 9x9-bit multiplication, or • One 36x36-bit multiplication • While configured in the 36x36 mode, the DSP block can also perform floating-point arithmetic.

  18. DSP IP Portfolio • Comprehensive • Constraint Driven

  19. IP Block example • Overclocking automatically used to reduce DSP slice count. • Quick estimates provided by IP compiler GUI • Insures best results for your design requirements.

  20. Altera: DFPAU • D-Floating Point Arithmetic Coprocessor. • Replaces C software functions by fast hardware operations – accelerates system performance • Uses specialized algorithms to compute arithmetic functions

  21. Altera: DFPAU

  22. Hardware circuit for FP adder • Breaking up an number into exponent and mantissa requires pre- and post-processing • Comprises • Alignment (100 ALMs) • Operation (21 ALMs) • Normalization (81 ALMs) • Rounding (50 ALMs) • Normalization and rounding together occupy half of the circuit area How to improve this?

  23. When not to use Floating Point? • Algorithms designed for fixed point • Greater precision and dynamic range are not helpful because algorithms are bit exact. • E.g. Transform to go to frequency domain in video codecs has some form of a DCT (Discrete Cosine Transform). • Designed to be performed on a fixed-point processor and are bit exact. Also, when precision is not as important as speed

  24. Constant Cache • Some applications load data from memory once and reuse it frequently • Could pose a bottleneck on performance. • What can we do? • Copying data to local memory  • may not be enough, as each work group would have to perform the copy operation • Solution • Create a constant cache that only loads data when it is not present within it, regardless of which workgroup requires the data i.e. FFT

  25. Datapath with a Constant Cache

  26. Example FFT Large computation, can be pre-computed

  27. Equalizer Example

  28. Routing Challenge

  29. Routing challenge • Designed performance achieved only when the datasets are readily accessed from fast on-chip SRAMs. • For large data sets, the main performance bottleneck is the off-chip memory bandwidth. • With DRAM, you can process data on stages with only a portion of dataset that fits on chip operated on at a time. • Available memory bandwidth determines performance.

  30. Routing Resources Xilinx: more local routing resources Altera: wide buses Also has value, because normally wide data vectors with 16 to 32 bits must be moved to the next DSP block. • Synergistic with DSP because most DSP algorithms process data locally.

  31. Example: Matrix Multiplication • Double-precisions FP cores (64 bits) • Matrix operations require all matrix element calculations to complete at the same time. • These parallelized or “vector” operations will occur at the slowest clock speed of all the FP functions in the FPGA.

  32. Routing Challenge • Hypothesis (constrained performance prediction): • Estimated 15 % logic unusable (due to data path routing, routing constraints, etc.) • Estimated 33 % decrease in FP function clock speed • Extra 24,000 ALUs for local SRAM memory controller and processor interface 39 +, 39 X Clock Speed: 200 Mhz Performance: 15.7 GFLOPS Peak is: 300 MHZ 25.5 GFLOPS

  33. Routing Challenge • Considerations: • Latency of transfer of A and B matrix from microprocessor to local FPGA SRAM not included in benchmark time. • Challenge when using all double-precision FP cores: feeding them with data on every clock cycle. When dealing with double-precision 64-bit data, and parallelizing many FP arithmetic cores, wide internal memory interfaces are needed.

  34. Routing Challenge: Results • Average sustained throughput : 88 percent. • 40 multiply and 40 adder tree cores – result every clock cycle • Five additional adder cores used for blocking implementation: one value per clock cycle • The GFLOPS calculation then is 200 MHz * 81 operators * 88 percent duty cycle = 14.25 GFLOPS. • Lower than expectation – due to the time needed to read and write values to the external SRAM. • With multiple SRAM banks providing higher memory bandwidth, the GFLOPS would be closer to the 15.7 GFLOPS number. • Power: • The expected 15 GFLOPS performance of the Stratix EP2S180 FPGA running at 30 W is close to the sustained performance of a 60-W 3-GHz Intel Woodcrest CPU

  35. FPGA implementations of fast Fourier transforms forreal-time signal & image processing I.S. Uzun, A. Amira and A. Bouridane

  36. Functional block diagram of 1-D FFT processor architecture

  37. AGU: Radix-2 DIF FFT • w s :¼ 1 • for stage :¼ log 2 ðNÞ to • 1 step 1 fnnstage loop • m :¼ 2^stage • is :¼ m=2 • w index0 :¼ 0 • for group :¼ 0 to n m step m • fnngroup loop • for bfi :¼ 0 to is l fnnbutterfly loop • Index0 :¼ r þ j • IEE Proc.-Vis. Image Signal Process., Vol. 152, No. 3, June 2005 295 • Index1 :¼ Index0 þ is; • } • w index0 :¼ w index0 þ w s; • } • w s :¼ w s 1