Neural Networks (MNIST inference) on the “3-cent” Microcontroller

Bouyed by the surprisingly good performance of neural networks with quantization aware training on the CH32V003, I wondered how far this can be pushed. How much can we compress a neural network while still achieving good test accuracy on the MNIST dataset? When it comes to absolutely low-end microcontrollers, there is hardly a more compelling target than the Padauk 8-bit microcontrollers. These are microcontrollers optimized for the simplest and lowest cost applications there are. The smallest device of the portfolio, the PMS150C, sports 1024 13-bit word one-time-programmable memory and 64 bytes of ram, more than an order of magnitude smaller than the CH32V003. In addition, it has a proprieteray accumulator based 8-bit architecture, as opposed to a much more powerful RISC-V instruction set.

Is it possible to implement an MNIST inference engine, which can classify handwritten numbers, also on a PMS150C?

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A LED-Candle based on the 3 cent MCU

After having reviewed sub $0.10 microcontrollers recently, it’s time for some projects using the Padauk PFS154 and PMS150C. Considering my previous investigation of electronic and non-electronic candles, it appears only natural to chose this as a target for the lowest cost microcontrollers.

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The “terrible” 3 cent MCU – a short survey of sub $0.10 microcontrollers.

Like many others, I was quite amazed to learn about a microcontroller sold for only 0.03 USD via the EEVblog last year. How was this possible? Many assumed this was a fire sale of an old product. Digging a bit further, it became apparent that there is an entire market segment of ultra-low-cost microcontrollers. Almost all of them are products of rather unknown companies from China or Taiwan. This write up summarizes my findings in this rather peculiar niche.

We already learned that there is a large variety of very powerful $1.00 microcontrollers, but what about the $0.10 MCU? Are they indeed all “terrible”, as suggested elsewhere?

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