Single Path One-Shot (SPOS): Efficient Neural Architecture Search with Simplified Supernet
Deep Learning
Optimization
Machine Learning
The paper introduces a novel approach called Single Path One-Shot (SPOS) for Neural Architecture Search (NAS). SPOS decouples architecture search from supernet training by using a simplified supernet with single paths and a uniform path sampling strategy, significantly improving efficiency and effectiveness. The method also incorporates channel search and mixed-precision quantization, leading to the discovery of accurate and resource-efficient neural network architectures.
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About this paper
SPOS addresses limitations of existing NAS methods by simplifying the supernet structure, utilizing an evolutionary algorithm, and incorporating channel search and mixed-precision quantization. The approach outperforms previous methods in accuracy, complexity, and resource efficiency. It demonstrates strong correlation between supernet and individual architecture performance, enhancing the search process efficiency.