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    • 1. 发明公开
    • ARTIFICIAL NEURAL NETWORK PROCESSING METHODS AND SYSTEM
    • EP3968235A1
    • 2022-03-16
    • EP21184536.7
    • 2021-07-08
    • STMicroelectronics S.r.l.
    • CARRERA, DiegoCOLELLA, MatteoDESOLI, GiuseppeBORACCHI, GiacomoROSSI, BeatriceFRAGNETO, PasqualinaFRITTOLI, Luca
    • G06N3/04
    • A computer-implemented method (40), comprising: providing an ANN processing stage having a plurality of processing layers with respective parameters including at least one set of weight parameters (w i ), at least one input, resp. output, activation parameter and at least one activation function parameter; setting to an integer value a dimensional parameter (d) of a lattice having a plurality of lattice points and identified by a set of basis vectors; selecting (400) a set of weight parameters (w i ) of a respective processing layer; applying vectorization processing (402) to the selected set of weight parameters (wi) producing a set of weight vectors arranged as items of a matrix of weight vectors (U) ; performing normalization processing (404) of the matrix of weight vectors (U), producing a matrix of normalized weight vectors (U'); applying lattice vector quantization, LVQ, processing (406) to the matrix of normalized weight vectors (U'), producing a codebook (CB) of codewords; applying indexing processing (408) comprising encoding codewords of the codebook (CB) as a function of the lattice, producing respective tuples of indices ( i s , i a , i a LUT ). Performing the normalization processing (404) comprises computing an optimization problem having a first term configured to provide normalized weight values approximating at least output, as a function of at least one input, activation parameter, and a regularization term Ω L ( u ), configured to amplify weight values having a short distance from the lattice points of the lattice L.