Last month, PyCon AU was held in Sydney, Australia, and a quarter of the Friday sessions were devoted to the Internet of Things. MicroPython played a big role at the event, with Damien George giving a talk on Writing Fast and Efficient MicroPython. To better understand what MicroPython is doing behind the scenes, and how to get the most out of it, this post dives into some of the technical details of how MicroPython works. General techniques for making things run faster are discussed, as well as ways to avoid memory allocations altogether, which is important for both efficient programming and deterministic code execution. Making devices run efficiently is also an important part of making them consume less power, which is important because there are so many embedded devices and reducing power consumption has real-world implications. Part of the discussion involved native capabilities in MicroPython, which compiles Python code directly to machine code so that it can run faster than bytecode. A lot of work has been done on MicroPython over the past month to improve native support and make it more efficient. The ultimate goal of this is to get close to fully supporting all Python features natively, adding support for native code in precompiled .mpy files, and allowing freezing of native code in firmware. In addition to watching the talk, you can also download a PDF of the talk slides as well as the demonstration code ledblink.py and fileread.py. Featured talks in the IOT Experts section were from MyPython:
Matt Trentini, Asyncio in Micropython[/url]
Stacy Morse, Lighting Macro Photographs with CircuitPython[/url]
Brian Danilko, Demystifying LoRaWAN with Pycom[/url]
During the main session, Tim Ansell also gave a very interesting five minute light talk where he talked about using MicroPython on FPGA, be sure to watch that! Thanks for reading, and stay tuned for further news on the upcoming new Pyboard D series. Original article: https://forum.micropython.org/viewtopic.php?t=5275&p=30234
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