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SOC FPGA Design Lab Discussion 5 SDR Lab Continued: DSP Blocks (Part I)

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Presentation on theme: "SOC FPGA Design Lab Discussion 5 SDR Lab Continued: DSP Blocks (Part I)"— Presentation transcript:

1 SOC FPGA Design Lab Discussion 5 SDR Lab Continued: DSP Blocks (Part I)

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3 Agenda Discuss overall DSP architecture –Clock domains Lab 4 Recap Channel selection filter –Design –CoreGen Implementation Discuss the tools which will be useful in development / verification of our design –UDP streaming of data (as in Lab 4) –ISIM / Modelsim –Chipscope Introduction

4 Radio Receiver Core /- 3 MHz 25 Msps Audio 25 Million 14-bit numbers / sec 48k samples / second

5 Multiple information channels exist in this signal, a typical job of the DSP blockset would be to –Tune to the appropriate section of spectrum –Mix it to baseband –Filter out other channels –Reduce data rate (decimate) –Demodulate Tuning / Downconversion Given that we have sampled at 25MHz, the input to the signal processing blocks is depicted below 0 (DC) 12.5MHz

6 Frequency Translation We are going to accomplish this tunable translation with a DDS for generation of a complex sinusoid, followed by a complex mixer. Details next week. Graphics reprinted from :

7 Filtering Two goals : –Remove contributions of signals from outside our channel –Reduce bandwidth so that data rate becomes manageable 25 Msps is not necessary to represent our small channel bandwidth (ex : 150 kHz for FM radio)

8 Lab 5 : Filter Demonstration Implement the channel selection filter which will be used in the SDR ADCL.P. Filter520 25Msps 48kHz FIFOuBlaze Ethernet to PC For analysis

9 FIR Filtering

10 FIR Implementation Convolution in time domain is computationally complex, yet fairly straightforward. Delay

11 Example Filter Design Process Filter Specs: –Fs = 25 Msps –75kHz passband –100k stopband –Attenuate stopband signals by > 60dB FIR Filter with <2048 taps will meet this requirement Matlab : fdatool

12 Scaling and Quantizing h[n] Output from FDAtool is floating point coefficients h[NUMTAPS] (aka impulse response). In absense of floating point multipliers, this is not directly implementable on our FPGA. We need to transform this impulse response to integers while preserving the function. Numint = int32(Num*ScaleFactor+0.5); Scaling factor, How to choose? Num

13 Quantized Coefficients Overall, making the coefficients integers, (after multiplying by 32700) doesnt affect our response too badly. With this scaling factor, our coefficient width is really only 9 bits. Some optimization between coefficient width and filter order could be undertaken if we chose.

14 freqz(numint,[1],100000); numint=round(Num*1024); numint=round(Num* );

15 Scaling and Quantizing h[n] Numint = int32(Num* ); fid=fopen('filt2030.coe','w'); fprintf(fid,'radix=10;\ncoefdata= \n'); fprintf(fid,'%d,\n',NumInt); fclose(fid); radix=10; coefdata= 15, -1, …etc Result is : impulse response of filter in a coe file, which we will use later when designing the filter. Note that filter gain has changed though over the unity gain filter we designed in Matlab. Now, signals in the passband will come out x32700 over the input level. Scaling factor, Choose this for a reasonable Performance vs. utilization tradeoff

16 FIR Implementation How many Multiply-Accumulate operations (MAC) are required per sample to implement the filter we designed? What is the overall rate of MACs per second? Delay

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18 Tool takes as input the COE file created previously to get h[n]

19 Implementation details show what we computed previously with a savings Due to symmetrical structure

20 As an example for high performance FPGA capabilities – consider Virtex 6. DSP48E slices run up to 600MHz clock rates Theoretical :172 GMACs / sec – 1.2 TMACs/sec

21 If we let filter run at higher clock rate than input sample rate, structure Is automatically adapted such that a convolution takes multiple clocks using Shared multipliers

22 Spartan 6 LX45 DSP 58 * 390M = max 22.6 GMACs / sec –Our FPGA is capable of doing about ½ of what we want –There will be other features of the FPGA that need multipliers… These figures should help put FPGA capabilities for DSP in perspective

23 Allowing the filter to use the entire time between output samples for the covolution makes the task easily achievable (even with a slower clock to the Filter)

24 Decimating FIR Filter Core (AXI-Stream Interface) COMPONENT channel_selector PORT ( aclk : IN STD_LOGIC; s_axis_data_tvalid : IN STD_LOGIC; s_axis_data_tready : OUT STD_LOGIC; s_axis_data_tdata : IN STD_LOGIC_VECTOR(15 DOWNTO 0); m_axis_data_tvalid : OUT STD_LOGIC; m_axis_data_tdata : OUT STD_LOGIC_VECTOR(31 DOWNTO 0) ); END COMPONENT; S_axis_data_tvalid s_axis_data_tdata m..tdata m..tvalid ACLK valid : enable signal, new data is latched on rising edge of clock when this is high Din : data to filter Ready : ready for new data valid : enable signal, result is present on output on rising edge of clock when this signal is high Changes with implementation and coeffs! tready

25 Issues / Decisions Clock domain for filter vs clock domain for A/D samples –25, 50, higher? –Higher clocks allow fewer multipliers and higher performance, but requires some thought at the clock domains Coefficient width / scaling –Less bits for coefficients will save RAM, but will decrease filter performance –Scaling factor and its effect on filter output will need to be understood and compensated for.. Full scale input (14 bits) should generally map to full scale output (16 bits) Evaluate the various output rounding / precision options in the FIR filter block to decide how to achieve this objective. Simulate if confused! Full precision output is straightforward, output = input * scaling factor FIFO size –Not super important to change yet; but with reduced data rate, you can now stream UDP data continuously and use a much smaller FIFO

26 Development Matlab Simulation –Use this to solve all the real DSP issues Mixer frequency for tuning Filter number of taps / coefficients Scaling issues –FAST, easy to change

27 Development Modelsim / VHDL Simulation –Is useful to prove functionality of your design in known situations –Sometimes difficult to fully model real world –Simulations of individual pieces (particularly those which you did not write) can be very informative Even more so than documentation COREGEN cores easily simulated

28 SOC Debugging Premise : –Full simulation often impractical –Visibility of internal signals is helpful to thoroughly debug / verify a design Even external signals can be difficult to probe on high density boards –To observe functionality of your system as it interacts with an unpredictable real world is crucial

29 SDR Debugging Build proven reliable datapipes first: –i.e. your UDP or serial port Build in the ability to send pieces of data from various points in the system out to be observed. –Ethernet data pipe developed in lab 4 can be used to grab data from different points in your signal processing chain Simply provide a means for different things to be written into the FSL input FIFO.

30 Chipscope Pro Internal FPGA Logic Resources are used to capture internal signals / events –Data is read out via JTAG cable Essentially a logic analyzer inside the FPGA FPGA resource limited

31 Example of Logic Analyzer view while system is running. Real data from target

32 Chipscope Pro Flows

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42 Notes ICON core uses a BSCAN resource much like the Microblaze MDM Debugger –Spartan 3A DSP has only 1! Effort beyond the scope of this demo is required to get both working concurrently –Online description will follow System without Microblaze, or without debuggable Microblaze is the easiest to experiment with first


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