2 papers
cs.LG2024
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Yiyang Zhao, Yunzhuo Liu, Bo Jiang +1
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresse…
cs.LG2024
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
Yiyang Zhao, Linnan Wang, Tian Guo
Deploying deep learning models requires taking into consideration neural network metrics such as model size, inference latency, and #FLOPs, aside from inference accuracy. This resu…