From Newsgroup: comp.ai.philosophy

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>
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From Newsgroup: comp.ai.philosophy

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
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From Newsgroup: comp.ai.philosophy

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.

--- Synchronet 3.20a-Linux NewsLink 1.114