troller

Oct 22, 2024 Last reply: 1 year ago 3 Replies
G


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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Depends on whether you mean implementing /their/ recognizer, or just implementing a recognizer that could be trained using their data set.


Any 8-bitter can easily handle the computations ... FP is not required


- fixed point fractions will do fine. The issue is how much memory is needed and what your target chip brings to the party.

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G

Eternal September is a good, no cost Usenet provider.

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D

Perhaps you misunderstood me. I’m not the author, I just posted beginning of a blog post and provided the link to the rest of it because it seemed interesting. The reason I didn’t post a whole thing is because there are quite few illustrations.

Blog post ends with:

“It is indeed possible to implement MNIST inference with good accuracy using one of the cheapest and simplest microcontrollers on the market. A lot of memory footprint and processing overhead is usually spent on implementing flexible inference engines, that can accomodate a wide range of operators and model structures. Cutting this overhead away and reducing the functionality to its core allows for astonishing simplification at this very low end.

This hack demonstrates that there truly is no fundamental lower limit to applying machine learning and edge inference. However, the feasibility of implementing useful applications at this level is somewhat doubtful.”

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D

It's fine to quote from a blog post or other such sources, as long as you make it clear that this is what you are doing (and that you are not quoting so much that it is copyright infringement). Your first post in this thread was formatted in a way that makes it clear and obvious that it was your own original words, written for the Usenet post - but apparently that was not the case. Remember, no one reading Usenet is going to click on random links in a post - we need very good reason to do so. So please, next time write some introductory or explanatory text yourself and make the whole thing clearer.

I think it is quite cool to hear that it is possible to do something like this on these 3-cent microcontrollers, but I would not expect anyone to use them in practice.

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