In this work, we build a differentiable analytical model to enable mapping-first design space exploration of deep learning accelerator designs. We also apply deep learning to adapt this model to the Gemmini accelerator's RTL implementation.
For more details, please refer to:
@inproceedings{
hong2023dosa,
title={DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators},
author={Charles Hong and Qijing Huang and Grace Dinh and Mahesh Subedar and Yakun Sophia Shao},
booktitle={IEEE/ACM International Symposium on Microarchitecture (MICRO)},
year={2023},
url={https://people.eecs.berkeley.edu/~ysshao/assets/papers/dosa-micro2023.pdf}
}
Requires python>=3.10.0
.
To install Python dependencies:
pip install -e .