On the Universal Approximation Properties of Deep Neural Networks Using MAM Neurons

Philippe Bich, Andriy Enttsel, Luciano Prono, Alex Marchioni, Fabio Pareschi, Mauro Mangia, Gianluca Setti, Riccardo Rovatti

Abstract: As neural networks are trained to perform tasks of increasing complexity, their size increases, which presents several challenges in their deployment on devices with limited resources. To cope with this, a recently proposed approach hinges on substituting the classical Multiply-and-ACcumulate (MAC) neurons in the hidden layers with other neurons called Multiply-And-Max/min (MAM) whose selective behavior helps identify important interconnections, thus allowing aggressive pruning of the others. Hybrid MAM&MAC structures promise a 10x or even 100x reduction in their memory footprint compared to what can be obtained by pruning MAC-only structures. However, a cornerstone of maintaining this promise is the assumption that MAC&MAM architectures have the same expressive power as MAC-only ones. To concretize such a cornerstone, we take here a step in the theoretical characterization of the capabilities of mixed MAM&MAC networks. We prove, with two theorems, that two hidden MAM layers followed by a MAC neuron with possibly a normalization stage is a universal approximator.

External IDs:dblp:journals/pami/BichEPMPMSR25