Artifact Evaluation for ANT [MICRO'22]

Publication

If you use ANT in your research, please cite our paper:

@inproceedings{guo2022ant,
  title={ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization},
  author={Guo, Cong and Zhang, Chen and Leng, Jingwen and Liu, Zihan and Yang, Fan and Liu, Yunxin and Guo, Minyi and Zhu, Yuhao},
  booktitle={2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)},
  pages={1414--1433},
  year={2022},
  organization={IEEE}
}

This repository contains the source code for reproducing the experiments in the paper "ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization" at MICRO'22.

ant_quantization contains the ANT framework with PyTorch.

ant_simulator contains the performance and energy evaluation of ANT.

Project Structure

├── ant_quantization                        # The ANT framework with PyTorch.
│   ├── antquant                            # Quantization framework of ANT.
│   ├── BERT
│   │   ├── download_glue_data.py           # Download for GLUE data.
│   │   └── scripts                         # Reproduce the experiment data in Figure 12.
│   ├── ImageNet
│   │   └── scripts                         # Download checkpoints and reproduce the experimental data in Figure 12.
│   ├── quant                               # Quantization CUDA kernel.
│   └── result                              # Our test results.
├── ant_simulator                           # The performance and energy evaluation of ANT.
│   ├── results                             # Our test results.
│   └── run_ant.py                          # The scripts are for reproducing the experiment data in Figure 13.

License

Licensed under an Apache-2.0 license.