Entropic Score metric: Decoupling Topology and Size in Training-free NAS

• URL: http://arxiv.org/abs/2310.04179v1
• Date: Fri, 6 Oct 2023 11:49:21 GMT
• Title: Entropic Score metric: Decoupling Topology and Size in Training-free NAS
• Authors: Niccol\`o Cavagnero, Luca Robbiano, Francesca Pistilli, Barbara Caputo, Giuseppe Averta
• Abstract summary: This paper contributes with a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations. A proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour.
• Score: 18.804303642485895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and computational resources to generate accurate models. To overcome these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations; ii) a cyclic search algorithm to separately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet classification.

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