Hello all,
After the MNIST on ZYNQ example, it is time to move to a better and more powerful platform ZYNQ Ultrascale (ARM is more powerful).
This FIR example is based on the previous example here.
Although this is preformed on ZYNQ rather than ZYNQ-Ultrascale+, both platforms are so similar.
In this example we are going to compare both software Python signal filter and HW accelerated filter.
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Lets start with the overall block-diagram.
fir_acc.pdf (385.5 KB)
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Take a closer look to the filter block
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Make sure both the AXI Master and Slave data width is 32 bit.
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The FIR filter block constants and setups:
-255,-260,312,-288,-144,153,616,1233,1963,2739,3474,4081,4481,4620,4481,4081,3474,2739,1963,1233,616,153,-144,-288,-312,-260,-255
- Synthesis and compile the design and copy the required files to the PYNQ folder.
The Jupyter notebook example is attached here as well
fir_filter.ipynb (3.5 KB)
Conclusion
The acceleration rate is over x14 compared to software-based filter:
