Abstract: Embodiments of the invention may include a system and method of automatically optimizing calculation of a butterfly transform by a processing unit. The processing unit may be adapted to perform atomic [N×N] matrix-matrix multiplication operations. Embodiments of the invention may include: receiving an input data matrix of dimensions [M×B], representing a batch of B input data vectors, each of length M; arranging the input data matrix into S section matrices of dimensions [N rows×K columns], wherein K>=N and K<=B; calculating a plurality of [N×N] coefficient matrices representing coefficients of the butterfly transform; and performing an iterative process of atomic [N×N] matrix multiplication operations between the [N×K] section matrices and corresponding [N×N] coefficient matrices, to produce an output matrix O, where output matrix O may represents a result of the butterfly transform on the batch of B input vectors.

Abstract: A method and system for detecting at least one object depicted in an image may include: receiving an input vector IN, comprising a plurality of location data elements, each representing a location of at least a portion of the at least one depicted object, calculating a 2-dimensional (2D) relation matrix REL_2D, where each entry represents a value of a relation operator, between location data elements i, and j; calculating a 2D mask matrix MASK_2D, where each entry is ‘1’ if the confidence of location data element i is greater than the confidence of location data element j, and ‘0’ otherwise; calculating a penalty vector PEN, based on REL_2D and MASK_2D; elementwise multiplying the confidence levels of vector IN and vector PEN, to produce a selection vector SEL; and selecting at least one location data element based on vector SEL as representing the location of the at least one depicted object.

Abstract: A method and system for detecting at least one object depicted in an image may include: receiving an input vector IN, comprising a plurality of location data elements, each representing a location of at least a portion of the at least one depicted object, calculating a 2-dimensional (2D) relation matrix REL_2D, where each entry represents a value of a relation operator, between location data elements i, and j; calculating a 2D mask matrix MASK_2D, where each entry is ‘1’ if the confidence of location data element i is greater than the confidence of location data element j, and ‘0’ otherwise; calculating a penalty vector PEN, based on REL_2D and MASK_2D; elementwise multiplying the confidence levels of vector IN and vector PEN, to produce a selection vector SEL; and selecting at least one location data element based on vector SEL as representing the location of the at least one depicted object.

Abstract: A system and method of automated optimization of a Neural Network (NN) model by at least one processor may include: receiving a pretrained NN model; constructing at least one master NN model, based on the pretrained NN model, each master NN model comprising a plurality of subnetworks; for each master NN model, constructing a router NN, adapted to direct one or more data instances of an input dataset to specific subnetworks of the master NN model; for each master NN model, calculating a utility score; and selecting a master NN model of the at least one constructed master NN models, based on the utility score.