DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators

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:

MICRO'23 DOSA Paper

@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}
}

Installation

Requires python>=3.10.0.

To install Python dependencies:

pip install -e .

ucb-bar/dosa

DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators

Installation