A computer-implemented method, comprising: providing an artificial neural network, ANN, processing stage comprising a plurality of processing layers (100, 102, 104, 106, 108) having respective processing layer parameters (L 1, L 2, L 3, L 4), said processing layer parameters (L 1, L 2, L 3, L 4) including at least one set of weight parameters (w i), at least one input activation parameter ( a j), at least one output activation parameter ( b j) and at least one activation function parameter ( ρ), setting a dimensional parameter (d) of a lattice to an integer value, said lattice having a plurality of lattice points and identified by a set of basis vectors (b 1, b 2), selecting a set of weight parameters (w i) of a respective processing layer of said plurality of processing layers (100, 102, 104, 106, 108) of said ANN processing stage, applying vectorization processing to said selected set of weight parameters (w i) producing a set of weight vectors (u 1, u 2, u 3) as a result, and arranging said set of weight vectors (u 1, u 2, u 3) as items of a matrix of weight vectors, performing normalization processing of said matrix of weight vectors, producing a matrix of normalized weight vectors (U') as a result, applying lattice vector quantization, LVQ, processing to said matrix of normalized weight vectors (U'), producing a codebook of codewords as a result, applying indexing processing to said produced codebook, said indexing comprising encoding codewords of the codebook as a function of the lattice L, producing respective tuples of indices ( i s, i a, i a LUT) as a result, providing said produced tuples of indices ( i s, i a, i a LUT) to a user circuit, wherein performing said normalization processing of said matrix of weight vectors comprises computing an optimization problem having: a first term configured to provide normalized weight values which approximate the at least output activation parameter ( b j) of the ANN processing stage as a function of the at least one input activation parameter ( a j) of the ANN processing stage, and a regularization term Ω L (u) configured to amplify normalized weight values that are closer to, having a reduced distance from, said lattice points of the selected lattice. providing an artificial neural network, ANN, processing stage comprising a plurality of processing layers (100, 102, 104, 106, 108) having respective processing layer parameters (L 1, L 2, L 3, L 4), said processing layer parameters (L 1, L 2, L 3, L 4) including at least one set of weight parameters (w i), at least one input activation parameter ( a j), at least one output activation parameter ( b j) and at least one activation function parameter ( ρ), setting a dimensional parameter (d) of a lattice to an integer value, said lattice having a plurality of lattice points and identified by a set of basis vectors (b 1, b 2), selecting a set of weight parameters (w i) of a respective processing layer of said plurality of processing layers (100, 102, 104, 106, 108) of said ANN processing stage, applying vectorization processing to said selected set of weight parameters (w i) producing a set of weight vectors (u 1, u 2, u 3) as a result, and arranging said set of weight vectors (u 1, u 2, u 3) as items of a matrix of weight vectors, performing normalization processing of said matrix of weight vectors, producing a matrix of normalized weight vectors (U') as a result, applying lattice vector quantization, LVQ, processing to said matrix of normalized weight vectors (U'), producing a codebook of codewords as a result, applying indexing processing to said produced codebook, said indexing comprising encoding codewords of the codebook as a function of the lattice L, producing respective tuples of indices ( i s, i a, i a LUT) as a result, providing said produced tuples of indices ( i s, i a, i a LUT) to a user circuit, wherein performing said normalization processing of said matrix of weight vectors comprises computing an optimization problem having: a first term configured to provide normalized weight values which approximate the at least output activation parameter ( b j) of the ANN processing stage as a function of the at least one input activation parameter ( a j) of the ANN processing stage, and a regularization term Ω L (u) configured to amplify normalized weight values that are closer to, having a reduced distance from, said lattice points of the selected lattice. a first term configured to provide normalized weight values which approximate the at least output activation parameter ( b j) of the ANN processing stage as a function of the at least one input activation parameter ( a j) of the ANN processing stage, and a regularization term Ω L (u) configured to amplify normalized weight values that are closer to, having a reduced distance from, said lattice points of the selected lattice.