---
license: mit
tags:
- hardyt
- layernorm
- dyt
- fpga
---

# harDyT: A Post-Training Hardware-Aware LayerNorm Replacement

**Code Repository:** [https://gitlab.laas.fr/lgauthier/harDyT](https://gitlab.laas.fr/lgauthier/harDyT)

**Associated Paper:** Accepted in IEEE ISCAS 2026, *To be published*

## Model Description

- **License:** MIT
- **Model Type:** Image Classification
- **Architecture:** ConvNeXt, DeiT, Swin, ViT, derivated from timm's implementations
- **Dataset:** ImageNet-1k

To evaluate the models:
- Clone the repository and setup the environment:
```bash
git clone https://gitlab.laas.fr/lgauthier/harDyT.git
cd harDyT
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
- Run the evaluation script:
```bash
python evaluate.py --model <model_name> --data-path <path_to_imagenet_root>
```
> This code will download the pre-trained model weights and evaluate the model on the ImageNet-1k validation set.
> Typically, run `python evaluate.py --model vit_base_patch16_224 --data-path imagenet_train_subsets_20k/subset_1` to evaluate the ViT-B model on a subset of ImageNet-1k that includes the validation set and 20k training samples.

Multiple models are available:
- ConvNeXt-B: `convnext_base`
- ConvNeXt-L: `convnext_large`
- DeiT-S: `deit_small_patch16_224`
- DeiT-B: `deit_base_patch16_224`
- Swin-S: `swin_small_patch4_window7_224`
- Swin-B: `swin_base_patch4_window7_224`
- ViT-S: `vit_small_patch16_224`
- ViT-B: `vit_base_patch16_224`
- ViT-L: `vit_large_patch16_224`

*TODO: add model accuracies*