UNICO: Unified Hardware Software Co-Optimization for Robust Neural Network Acceleration

作者：Bahador Rashidi, Chao Gao, Shan Lu, Zhisheng Wang, Chunhua Zhou, Di Niu, Fengyu Sun · 年份：2023 · DOI：10.1145/3613424.3614282 · 被引用次数：6 · 研究领域：Fault Detection and Control Systems、Advanced Neural Network Applications、Adversarial Robustness in Machine Learning

Specialized hardware has become an indispensable component to deep neural network (DNN) acceleration. To keep up with the rapid evolution of neural networks, holistic and automated solutions for jointly optimizing both hardware (HW) architectures and software (SW) mapping have been studied. These studies face two major challenges. First, the combined HW-SW design space is vast, which hinders the finding of optimal or near-optimal designs. This issue is exacerbated for industrial cases when cycle accurate models are used for design evaluation in the joint optimization. Second, HW design is prone to overfitting to the input DNNs used in the HW-SW co-optimization. To address these issues, in this paper, we propose UNICO, an efficient Unified Co-Optimization framework with a novel Robustness metric for better HW generalization. Guided by a high-fidelity surrogate model, UNICO employs multi-objective Bayesian optimization to effectively explore the HW design space, and conducts adaptive, parallel and scalable software mapping search based on successive halving. To reduce HW overfitting, we propose a HW robustness metric by relating a HW configuration’s quality to its sensitivity in software mapping search, and quantitatively incorporate this metric to search for more robust HW design(s). We implement UNICO in open source accelerator platform, and compare it with the state-of-the-art solution HASCO. Experiments show that UNICO significantly outperforms HASCO; it finds design(s) wit...