Carbon-Efficient Neural Architecture Search
Yiyang Zhao Tian Guo
This work presents a novel approach to neural architecture search (NAS) that
aims to reduce energy costs and increase carbon efficiency during the model
design process. The proposed framework, called carbon-efficient NAS (CE-NAS),
consists of NAS evaluation algorithms with different energy requirements, a
multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS
dynamically balances energy-efficient sampling and energy-consuming evaluation
tasks based on current carbon emissions. Using a recent NAS benchmark dataset
and two carbon traces, our trace-driven simulations demonstrate that CE-NAS
achieves better carbon and search efficiency than the three baselines.