DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific characteristics. However, it may fail in promising the highest composite score which combines the goals of accuracy and other hardware-related constraints (e.g., latency, energy efficiency) when building a specific neural-network-based system. In this work, we present a single-stage automated framework, YOSO, aiming to generate the optimal solution of software-and-hardware that flexibly balances between the goal of accuracy, power, and QoS. Compared with the two-stage method on the baseline systolic array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or 1.79x~3.07x latency reduction at the same level of precision, for different user-specified energy and latency optimization constraints, respectively.
Cite
@article{arxiv.2005.07075,
title = {You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design},
author = {Weiwei Chen and Ying Wang and Shuang Yang and Chen Liu and Lei Zhang},
journal= {arXiv preprint arXiv:2005.07075},
year = {2020}
}