CNNGen: A Generator and a Dataset for Energy-Aware Neural Architecture Search
Antoine Gratia, Hong Liu, Shin’ichi Satoh, Paul Temple, Pierre‐Yves Schobbens, Gilles Perrouin · 2024
Neural Architecture Search (NAS) methods seek optimal networks by exploring thousands of variants of a reference architecture.Yet, optimality is typically related to prediction performance, overlooking the environmental impacts of training.Thus, NAS search spaces are unfit for performance and energy consumption trade-offs.We contribute to energy-aware NAS with (i) a grammar-based Convolutional Neural Network generator (CN-NGen) producing diverse architectures not based on a reference one; (ii) 1,300 available architectures obtained via CNNGen with their implementation, energy consumption and performance measurements; (iii) Three state-of-the-art predictors releasing the need for trained models for performance and energy estimation. CNN Generator (CNNGen)CNNGen uses the Xtext context-free grammar framework [3] to generate CNN architectures.The sequence of grammar tokens describes the CNN's topology (i.e., the succession of layers).Our grammar captures the CNN domain knowledge to produce valid architectures.Thus, CNNGen differs from other NAS methods like NASBench [2] Indeed, CNNGen produces architectures from scratch and not as variants of existing ones.CNNGen also comes with an editor allowing to specify architectures.From a valid sequence of grammar tokens, 173