Carbon-Efficient Neural Architecture Search
This work addresses energy consumption issues for researchers and practitioners in machine learning, though it is incremental as it builds on existing NAS methods.
The paper tackles the problem of high energy costs in neural architecture search by proposing a carbon-efficient framework that balances energy-efficient sampling and energy-consuming evaluation tasks, achieving better carbon and search efficiency than three baselines in trace-driven simulations.
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.