# Some novel DL workloads in 2021

## Table of Contents

Most Deep Learning Systems/Compilers in 2021 are still trying to optimize workloads like ResNet & VGG, though these models are really useful and we can publish papers on decent conference by proposing new compiler techniques for them (e.g. TASO/PET). I'm not so enthusiastic about competing on this track, Graph Neural Networks brings structured data into Deep Learning, which is good, but unfortunately the application of large scale GNNs are mostly limited to recommendation systems. I always expect my work to have broader positive social impact and apparently recommendation system can't.

I started surveying the evolving Deep Learning workloads this semester and try to find the "future" of Machine Learning Systems. Currently I feel like **AlphaFold**, **Diffusion Model** and **Neural Radiance Field** as three models that worth investigating into.

## AlphaFold

### TODO Multiple Sequence Alignment

### TODO AlphaFold

### TODO SE(3)-Transformer

#### Tensor Field Networks

\[ \textbf{f}(\textbf{x}) = \sum_{j=1}^{N} \textbf{f}_j \delta(\textbf{x} - \textbf{x}_j)\]

#### NVIDIA's optimization of SE(3)-Transformer

NVIDIA released an acceleration of SE(3)-Transformer.

### RoseTTAFold

## Diffusion Model

Lillian Weng's blog post is a good start point to know about diffusion models.