Is it possible to use the Python configuration language to configure a Field Programmable Gate Array (FPGA)? In this article, you will find out if that’s possible and if so, how to leverage the programming language.
Understanding the Programmability
One of the facts to note about python for FPGA is that the programmability takes root in the design concept. Ideally, programming an FPGA involves writing an executing the codes, the same way we would have with a software algorithm.
However, the process seems to be limiting, considering that the processes for writing the codes are limited. Ideally, developers have to heavily rely on either the C or C++ programming languages.
A couple of other solutions are using the Verilog HDL or the VDHL programming languages. Today, it is possible to use Python for FPGA. Keep reading to discover how this can be done.
Programming with Python for FPGA on Xilinx
Xilinx, now a part of AMD released the Python configuration option for Field Programmable Gate Arrays (FPGAs). The company did this to help bolster the performance of building programmable devices, especially those manufactured by Xilinx.
The solution was the Python Productivity for Zynq (PYNQ), an open-source platform for the Xilinx Zynq devices.
You may be wondering why the PYNQ platform was launched. Xilinx launched it to help cut down on some of the challenges with building programmable devices.
As an open-source project, PYNQ allows Xilinx device developers to use the platform to explore to the maximum, the possibilities of building FPGAs with the Python programming language.
Underlying Technology
Jupyter Notebook underscores the performance of the PYNQ platform. The notebook is a browser-based interactive computing environment that includes hardware libraries, targeted at enabling the development of different components, ranging from images, live codes, videos and explanatory texts. Other supported documents are:
- Equations
- Interactive widgets
- Equations
Worthy of mention is that these hardware libraries, also called overlays, enable FPGA developers to speed up or scale the software running on either the Alveo or Zynq circuit boards.
Some of the benefits of working with the PYNQ platform for Python for FPGA development are:
- Reducing latency control of the circuit boards.
- Parallel hardware execution
- Higher bandwidths for the Inputs and Outputs (I/Os)
- Real-time signal processing
- High frame-rate video processing
Xilinx Vitis
There are several other configuration solutions out there for FPGA development and Xilinx is once again, leading the pack. The circuit board manufacturer has what it calls Vitis, a dedicated toolchain or Artificial Intelligence (AI) framework that enables the integration of existing software solutions into FPGAs.
Worthy of mentioning is that the Vitis AI may not be a replacement for existing solutions, but it sure introduces newer concepts as they pertain to Python for FPGA.
For example, the Xilinx Vitis is a unified software platform that uses cutting-edge application to streamline the configurations of FPGAs meant for the AI market. It also has a dedicated set of I/Os and pre-programmed libraries that feature pre-accelerated functions for the data compression, AI, and vision markets.
Conclusion
In response to the question on whether Python programming can be used to program FPGAs, the answer is yes. Get started today with the Xilinx PYNQ platform and start programming Zynq RFSoCs, Zynq, Zynq UltraScale+ and Alveo circuit boards with the Python programming language.
