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?

…

…

<https://cpldcpu.wordpress.com/2024/05/02/machine-learning-mnist-inference-on-the-3-cent-microcontroller/>

<https://archive.md/DzqzL>

On 10/21/2024 3:06 PM, D. Ray wrote:
> 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?
>
> …
>
> …
>
> <https://cpldcpu.wordpress.com/2024/05/02/machine-learning-mnist-inference-on-the-3-cent-microcontroller/>
>
> <https://archive.md/DzqzL>

test to see if this posts or I should dump this paid provider.

--
Copyright 2024 Olcott

"Talent hits a target no one else can hit;
Genius hits a target no one else can see."
Arthur Schopenhauer

olcott <NoOne@NoWhere.com> wrote:
> On 10/21/2024 3:06 PM, D. Ray wrote:
>> 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?
>>
>> …
>>
>> …
>>
>> <https://cpldcpu.wordpress.com/2024/05/02/machine-learning-mnist-inference-on-the-3-cent-microcontroller/>
>>
>> <https://archive.md/DzqzL>
>
> test to see if this posts or I should dump this paid provider.

It worked.