Abstract: Systems and methods for optimizing artificial neural network (ANN) computations based on automatic determination of a batch size are disclosed. An example method may comprise receiving, by an optimization module, an ANN structure associated with the ANN, and generating, based on the ANN structure, a configuration for a computation engine capable of performing computation of the layers of the ANN. The configuration may include information concerning a batch size of one or more layers of the ANN. The batch size of a layer can be determined based on a bandwidth required to read data related to layer, a number of parameters associated with the layer, and a time the layer processes one input dataset from the batch. The batch size of the layer can differ from the batch size of the ANN. The batch size of the layer may differ from a batch size of another layer of ANN.

Abstract: Systems and methods for accelerating neuron computations in artificial neural network (ANN) are provided. An example method may comprise receiving, for calculation of a neuron of an ANN, a plurality of first values represented by A bits and a plurality of second values represented by B bits, splitting each value of the plurality of the first values into a set of parts, a count of bits of each of set of parts being less than A, to obtain a set of pluralities of parts, selectively performing mathematical operations on a first plurality of the set of pluralities and the plurality of the second values to obtain a first result, selectively performing further mathematical operations on further pluralities of the set of pluralities and the plurality of the second values to obtain further results, and determining, based on the first result and the further results, an output of the neuron.

Abstract: A system for increasing quality of results of computations of an artificial neural network (ANN) by using complex rounding rules for parameters in the ANN is provided, the system comprising one or more processing units configured to: receive a plurality of first parameters for one or more neurons of ANN, the first parameters being of a first data type; and change the first parameters to second parameters of a second data type to obtain a plurality of the second parameters according to a rule in which a distance between at least one first parameter and corresponding second parameter is greater than a distance between the first parameter and a value of the second data type closest to the at least one first parameter. A distance between a vector of the first parameters and a vector of the second parameters is minimized.

Abstract: Systems and methods for modifying a structure of an artificial neural network (ANN) are provided. An example method comprises receiving, by one or more processing units, a plurality of arrays of weights associated with the ANN, modifying, by the processing units, the plurality of arrays of weights to generate a further plurality of further arrays of weights, where after the modification the following conditions are satisfied: an amount of operations required for computing neurons of the ANN using the further plurality of further arrays of weights is less than an amount of operations required for computing same neurons of the ANN using the plurality of arrays of weights; and outputs of the neurons of the ANN computed using the plurality of arrays of weights are substantially equal to further outputs of the neurons of the ANN using the further plurality of further arrays of weights.

Abstract: Systems and methods for accelerating artificial neural network computation are disclosed. An example may comprise selecting, by a controller communicatively coupled to a selector and an arithmetic unit and based on a criterion, an input value from the stream of input values of a neuron, configuring, by the controller, the selector to provide, dynamically, the selected input value to the arithmetic unit, providing, by the controller to the arithmetic unit, an information of the selected input value, acquiring, by the arithmetic unit and based on the information, a weight from a set of weights, and performing, by the arithmetic unit a mathematical operation on the selected input value and the weight to obtain a result, wherein the result is to be used to compute an output of the neuron. The criterion may include a comparison between the input value and a reference value. The reference value may include zero.