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Title: Adafruit's New CircuitPython 'Turbo' Brings Native Code To Tiny Boards

Link: https://news.slashdot.org/story/26/09/15/0149...

Targeting students and beginners, Adafruit released "CircuitPython" in 2017
(as a derivative of the MicroPython microcontroller-optimized programming
language). Now Adafruit managing director Phillip Torrone (also long-time
Slashdot reader ptorrone) brings this update: Adafruit has published
CircuitPython Turbo, a workflow that compiles selected Python functions into
native machine code on a computer, then loads them onto compatible
microcontroller boards. It builds on MicroPython's Native and Viper emitters.
In a documented Metro RP2040 fixed-point Mandelbrot test, Viper cut
computation time from 8.335 seconds to 0.423 seconds, a 19.71x speedup over
bytecode. The rest of the application stays in Python. The guide includes
benchmarks, source code and hardware demos. The speedup is for the measured
computation, not the whole application. "Turbo support is now included in the
latest official CircuitPython builds for RP2040 and RP2350 boards..."
explains Torrone's announcement at Adafruit.com. "The new Turbo in
CircuitPython helps when the board spends time calculating: making neopixel
effects, drawing fractals, processing audio, filtering sensor readings, or
preparing lots of pixels. Those projects can get smoother animation, quicker
responses, or room to do more things at once." With Turbo, it's easier,
better, and now even faster to make LED light up costumes that also reacts to
sound at the same time, a sensor dashboard with animated graphics, or a tiny
game doing physics while drawing the screen. Turbo speeds up the busy Python
parts. It won't make a slow sensor or display connection faster... Your
computer turns selected functions into instructions the chip can run
directly. Python still handles the rest. We have measured speedups, real
display captures, and examples you can pull apart to see what happened. None
of this arrived alone. CircuitPython, MicroPython, PyMCU, compiler tools,
open hardware, and people sharing their work gave us pieces to connect. The
Bao experiments take that idea somewhere else, handing calculations to four
helper cores. Now we get to make those paths easier to use, compare results,
and find the next useful thing. Maybe that's smoother animation, a responsive
instrument, or an idea we haven't tried yet. That's what I like about open
source. Someone shares a piece, someone else sees a possibility, and we get
to keep building it together.

Read more of this story at Slashdot.

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