Abstract: The present invention relates to a method for pruning a neural network comprising a plurality of neurons, said method comprising: an initialization phase, wherein input information is fetched comprising at least parameters ({wni,bni}) related to said neural network and a dataset (D) representative of a task that said neural network has to deal with, wherein said parameters ({wni,bni}) comprising a weights vector (wni) and/or a bias (bni) related to at least one neuron of said plurality of neurons; a regularization phase, wherein said neural network is trained according to a training algorithm by using said dataset (D); a thresholding phase, wherein an element (wnij) of said weights vector (wni) is put at zero when its absolute value is below a given threshold (T).

Abstract: A method includes providing a neural network having a set of weights. The neural network receives an input data structure for generating a corresponding output array according to values of the set of weights. The neural network is trained to obtain a trained neural network. The training includes setting values of the set of weights with a gradient descent algorithm which exploits a cost function including a loss term and a regularization term. The trained neural network is deployed on a device through a communication network, and used by the device. The regularization term is based on a rate of change of elements of the output array caused by variations of the set of weights values.