CNAS: Constrained Neural Architecture Search

Date: 2022-10-12

Venue: International Conference on Systems, Man, and Cybernetics (IEEE SMC), Prague, 2022

Neural Architecture Search (NAS) paves the way for the automatic definition of neural networks architectures. The research interest in this field is steadily growing with several solutions available in the literature. This study introduces, for the first time in the literature, a NAS solution, called Constrained NAS (CNAS), able to take into account constraints on the search of the designed neural architecture. Specifically, CNAS is able to consider both functional constraints (i.e., the type of operations that can be carried out in the neural network) and technological constraints (i.e., constraints on the computational and memory demand of the designed neural network). CNAS has been successfully applied to Tiny Machine Learning and Privacy-Preserving Deep Learning with Homomorphic Encryption being two relevant and challenging application scenarios where functional and technological constraints are relevant in the neural network search.

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