
Machine Learning on 2KB of RAM [pdf] - gyre007
http://manikvarma.org/pubs/kumar17.pdf
======
snops
I have previously seen Microsoft Embedded Learning Library[1] which requires
LLVM support for the target arch IIRC, and uTensor[2] which runs TensorFlow
models on larger ARM cortex micros.

Nice to see another group in Microsoft is targeting smaller 8/16 bit
processors, they can still be useful for very limited power budget
applications, low cost devices, and some odd applications where they are built
in to a specific purpose IC (e.g. a flash drive controller). There aren't many
other alternatives in this space I think, other than manually porting your
model to C and running unit tests against it, does anyone know of any
competitors?

[1][https://github.com/Microsoft/ELL](https://github.com/Microsoft/ELL)
[2][https://github.com/uTensor/uTensor](https://github.com/uTensor/uTensor)

~~~
jononor
Also interested in more competitors, as I'm researching and developing
machinelearning for microcontrollers. Two libraries I have made are:

[https://github.com/jonnor/emtrees](https://github.com/jonnor/emtrees)
[https://github.com/jonnor/embayes](https://github.com/jonnor/embayes)

These are likely to be consolidated into one library/framework in some weeks,
along with some other models and tools I have lying around. Like simple neural
networks and audio feature extraction.

~~~
jononor
My brain dump with links can be found here,
[https://github.com/jonnor/datascience-
master/blob/master/emb...](https://github.com/jonnor/datascience-
master/blob/master/embeddedml/README.md)

------
tahw
How could this possibly be faster than a linear classifier? In
"Implementation" near Section 2 they claim that their implementation is better
than a linear classifier but that seems like it couldn't possibly be true
could it? A single floating point operation per parameter has gotta be faster
than multiple branches, right?

~~~
jononor
A linear classifier can only handle linearly separable things, which limits
prediction accuracy a lot. I don't think you'll get anywhere close to this
level of classification with a linear model. In the experiments it is shown to
outperform SVM RBF.

The platforms in question doesn't have hardware for floating point, any
floating point support needs to be emulated in software (slow).

