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Accelerating DSP Algorithms Using FPGAs. Sean Gallagher DSP Specialist Xilinx Inc. Why DSP in FPGAs. Availability of fast analog-to-digital converters (ADCs) Enables digital methods for functions traditionally done in RF components Massive parallel processing

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Accelerating dsp algorithms using fpgas

Accelerating DSP Algorithms Using FPGAs

Sean Gallagher

DSP Specialist

Xilinx Inc


Why dsp in fpgas
Why DSP in FPGAs

  • Availability of fast analog-to-digital converters (ADCs)

    • Enables digital methods for functions traditionally done in RF components

  • Massive parallel processing

    • FPGAs may have several hundred embedded multipliers on-chip

    • One FPGA can replace many DSP Processors


Architectural considerations
Architectural Considerations

  • FPGA architectures are vendor specific

    • Unlike ASICS, no two are alike

  • FPGA vendors develop distinct competencies

    • In device architecture design

    • In intellectual property (dsp functions, bus controllers, etc)

    • In design tool flows

  • Vendor independent HDL can be written but this usually achieves mediocre results in clock speed and design size instantiation


Fpgas are massive parallel computing machines
FPGAs Are Massive Parallel Computing Machines

80MHz Samples

ch1

20MHz Samples

LPF

  • FPGAs are ideally suited for multi-channel DSP designs

    • Many low sample rate channels can be multiplexed (e.g. TDM) and processed in the FPGA, at a high rate

    • Interpolation (using zeros) can also drive sample rates higher

ch2

LPF

LPF

ch3

LPF

Multi Channel

Filter

ch4

LPF


Fpgas allow space speed trade offs

×

×

×

×

+

+

+

+

+

+

FPGAs Allow Space/Speed Trade-offs

A

Q = (A x B) + (C x D) + (E x F) + (G x H)

can be implemented in parallel

B

C

D

Q

E

F

G

H

But is this the only way in the FPGA?


Customize architectures to suit your ideal algorithms

×

×

×

×

×

×

×

D Q

+

+

+

+

+

+

+

+

+

+

+

+

Customize Architectures to Suit your Ideal Algorithms

FPGAs allow Area (cost) / Performance tradeoffs

Parallel

Semi-Parallel

Serial

D Q

Speed

Optimized for?

Area


Exploitng the xilinx architecture for dsp functions
Exploitng The Xilinx Architecture For DSP Functions

  • Memory Blocks that can be configured as ROMs, dual port RAMs, FIFOs

  • Embedded 18x18 multipliers that can be ganged to form a 35x35 bit multiply

  • SRL16 shift registers

    • A patented technique for turning the 4 input lookup table (2 per slice) into an addressable shift register


Using srl16e to increase compute density
Using SRL16E to increase Compute Density

20MHz

4 channels

9

9

9

k3

k2

k1

k0

9

18

+

+

+

+

‘0’

9

SRL16E takes the same area as one LUT.

It can be used for up to 16 channels.

9 channels

9

20MHz

k3

k2

+

+

‘0’


Xilinx system generator for dsp
Xilinx System Generator For DSP

  • System Generator is a Block Set that resides in Simulink/Matlab environment.

  • System Generator blocks are bit true and cycle true models of Xilinx’s DSP intellectual property (IP) cores.

  • Hardware DSP design capture is significantly accelerated due to automatic code generation from Simulink


Algorithm instantiation considerations
Algorithm Instantiation Considerations

  • There are cases where following a textbook approach does not necessarily translate into an efficient instantiation

  • Manipulating the algorithm to exploit features of the architecture can lead to much more efficient instantiations

  • Modification of a text book algorithm includes how the math is executed as well as over-clocking structures to allow the structures to be time division multiplexed


Example 1 digital down conversion
Example 1: Digital Down Conversion

  • In digital down conversion we need to filter before we decimate to prevent aliasing

  • These filters can get rather large because the transition band is rather narrow in relation to the sample rate

  • A text book solution is to step the sample rate down in steps


Digital down conversion
Digital Down Conversion

  • The following 3 slides show three different filter designs for the down conversion of a .625 Mhz band of interest that is centered at 20 MHz and sampled at 61.44 MHz.

    • The decimation rate is 25

    • The final sample rate will be 61.44/25= 2.4576MHz

  • The next slide shows the filter design needed if decimating by 25 in one step

    • the total coefficient count is 184

  • The two slides after the next show the two filters necessary to decimate in steps, decimating by 5 in each step

    • The total coefficient count is 11+43=54


Digital down conversion ddc implementation
Digital Down Conversion (DDC) Implementation

  • The following design shows how the DDC function would be implemented using the FIR filter core from the Xilinx Library

  • The coefficients are automatically loaded into the filter cores

  • The design has been compiled and was found to use about 6000 logic slices

  • The fir filter core is a legacy core and is built as an optimized lookup table of coefficients



Ddc another way
DDC –Another Way

  • While we were able to exploit the math of DSP to reduce our coefficient count, we did not necessarily exploit the Xilinx architecture.

  • The next design shows a design that implements the 184 coefficient filter but is significantly smaller in instantiation size then the previous design

  • This design exploits the memory, embedded multipliers, and SRL16s


Time division multiplexed input
Time Division Multiplexed Input

Multiplexing I&Q multiplication so that

just one filter is needed instead of two


Efficient shift registers via srl16s
Efficient Shift Registers via SRL16s

Delay line would require 16x50x7=5200 registers

which would be 2800 logic slices.

Use of SRL16s reduces slice count to less then 700


Clock based demuxing and automatic pipeline balancing
Clock Based Demuxing And Automatic Pipeline Balancing

Down sample block grabs last sample in a frame

Delay block “slide” frame

Balancing latencies is a common requirement in DSP designs. The Sync block uses SRL16s (very efficient) to automatically balance pipeline delays

Down sample block grabs next sample in a frame


Notes on previous design
Notes on Previous Design

  • One filter structure is used by clocking the filter at twice the rate of the incoming data

  • The coefficients are stored in memory, 25 per rom. There are 200 coefficients but this approach allows storage of many more

  • The delay between taps is built using SRL 16s. This would have taken 2800 slices alone without SRL16s but instead the entire design is less that 700 slices


Channelizer design
Channelizer Design

  • The following design is a 64 channel channelizer based on the technique known as polyphase decimation filter with a DFT bank

  • The design basebands and decimates 64 channels simultaniously

  • The polyphase decimation is the same structure as the previous design, hence very efficient device utilization.

  • This filter structure uses the on-chip ram blocks of the Xilinx device to store the coefficients

  • This technique requires a tapped shift register that requires 6272 registers (3136 slices). However, Xilinx’s patented ability to turn the logic look-up table into a 16 bit register reduces this require by more than an order of magnitude. The whole design is less than 1700 slices.

  • The DFT is implemented with a streaming fft core. The streaming mode allows the FFT to keep up with the data rate

  • Individual channels out of the fft are demuxed using the implied clocking technique seen in the previous design


512 Coefficients are stored in on chip block rams

64 pt FFT set to streaming mode


Filter coefficients are stored in on-chip block rams.

A new phase of the 64 phase-polyphase filter is rotated into

the multipliers on every clock cycle.

There are 64 phases x 8 taps =512 coefficients


Conclusion
Conclusion

  • Efficient FPGA instantiation of DSP algorithms requires exploitation of the FPGA vendor’s architecture. Xilinx’s Virtex II architecture is especially amenable to systolic computation structures

  • FPGA architectures may present non-obvious instantiation choices that are more efficient then a typical textbook approach

  • Algorithms can and should be modified for parallelized data flow instantiation.


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