This project recently hit Hackaday front page. So this seems a good time for a quick update, and maybe some clarifications.

What this project is

1. Research into ultra-low cost hardware for TinyML systems.

The motivation is to explore what is possible within an artificially constrained budget. And what are the implications on the software and ML side of the computational constraints such an environment has.

2. Testing grounds for the emlearn open-source software package. The software is mostly used on slightly more powerful microcontrollers, typically 0.5-5 USD for just the microcontroller, and similar amounts in sensors. But trying to scale down is a good torture test.

What this project is not

1. NOT a good starting point for getting into ML on microcontrollers and sensors (TinyML).

For that, I recommend getting much beefier hardware. Like an ESP32 with several megabytes of RAM and FLASH. That will be a lot more practical and fun. AdaFruit, Seed Studio, Sparkfun, Olimex etc all have good options. Arduino with Tensorflow Lite for Microcontrollers is probably the most practical software starting point still. I am working on MicroPython bindings for emlearn which has the goal to be super accessible. But that project is still in very early days.

2. NOT a ready-to-run board

Current rev0 boards have just been through basic HW bringup - with several critical problems for actual usage. But looks to be enough to continue testing on - which is all that matters for a rev0 board. A new board revision will come some time in the summer, after I have had time to test and develop some more. That might actually be usable, if we are lucky.
The BLE driver and firmware is also just skeletons at this point in time.

News

CNN running on PY32. I have been testing running some Convolutional Neural Networks on Puya PY32. I was able to port TinyMaix successfully, and run a 3 layer CNN that takes 28x28 dimensional input. This complexity would be suitable for doing simple audio recognition - which is of interest in this project. However, it used 2 kB RAM and 25 kB of FLASH - leaving only 2 kB RAM and 7 kB FLASH for the rest of the system. That would be a tight squeeze... But they claim the AVR8 port used only 12 kB FLASH - so maybe it can be optimized down. To be investigated....

emlearn + MicroPython presentation at PyData Berlin. The slides are available. Video is to be published in the coming weeks, I believe.

Going to TinyML EMEA 2024 in Milano, Italy in June. I will be presenting about the emlearn TinyML software project. And maybe also a little bit about this hardware project :)