> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cellulose.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Enable more Deep Learning Optimization Workflows

Maziar Raissi put together a [great paper](https://arxiv.org/abs/2301.11316) on
some open problems in applying deep learning:

> There are times when not only we are looking for the
> most performant model but also we want the model to be as memory and
> compute efficient as possible. This is an important stepping stone towards
> democratizing artificial intelligence in anticipation of the future of Internet
> of Things where a lot of our devices (e.g., cellphones, cars, security cameras,
> refrigerators, air conditioners, etc.) will be intelligent. Such devices usually
> have smaller compute capabilities and memory capacity than our computers in
> data-centers or on the cloud. To make them intelligent we need to
> take their constraints into consideration.

There's also a whole [YouTube playlist](https://www.youtube.com/playlist?list=PLoEMreTa9CNk2TXiWDl0i-Gsou3ejD7za)
on some of these techniques.

Based on personal experience, even "low hanging fruit" optimizations can halve
your model's total inference latency, potentially saving your GPU cloud costs
too. Unfortunately, a lot of these deep learning techniques aren't adopted
today. They're probably too cumbersome to add to an already complex AI workflow.

Cellulose wants to encourage machine learning engineers to adopt these but
have the process to be as "one click" as possible. We'll start by adding
NVIDIA quatization support, then adding methods to specify calibration datasets.
We'll then look into other more advanced methods like compression / pruning and
Neural Architecture Search (NAS) in the future - all configurable and tuneable
from the dashboard.

## Have questions / need help?

Please reach out to [support@cellulose.ai](mailto:support@cellulose.ai), and we'll get back to you as soon
as possible.