CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
CoRR(2024)
摘要
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 addresses the key challenge of high carbon cost associated
with NAS by exploring the carbon emission variations of energy and energy
differences of different NAS algorithms. At the high level, CE-NAS leverages a
reinforcement-learning agent to dynamically adjust GPU resources based on
carbon intensity, predicted by a time-series transformer, to balance
energy-efficient sampling and energy-intensive evaluation tasks. Furthermore,
CE-NAS leverages a recently proposed multi-objective optimizer to effectively
reduce the NAS search space. We demonstrate the efficacy of CE-NAS in lowering
carbon emissions while achieving SOTA results for both NAS datasets and
open-domain NAS tasks. For example, on the HW-NasBench dataset, CE-NAS reduces
carbon emissions by up to 7.22X while maintaining a search efficiency
comparable to vanilla NAS. For open-domain NAS tasks, CE-NAS achieves SOTA
results with 97.35
carbon consumption of 38.53 lbs of CO2. On ImageNet, our searched model
achieves 80.6
NVIDIA V100, consuming only 909.86 lbs of CO2, making it comparable to other
one-shot-based NAS baselines.
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