Abstract: Methods and systems for generating an integer neural network are described. The method includes receiving an input vector comprising a plurality of input values. The plurality of input values are represented using a desired number bits. The input vector is multiplied by a weight vector, and the products of which are summed to obtain a first value. The first value is quantized and applied to a piecewise linear activation function to obtain a second value. The piecewise linear activation function is a set of linear function that collectively approximate a nonlinear activation function. The second value is quantized to generate the output of the neuron in the integer neural network.

Abstract: Systems and methods for training a machine learning model. The methods comprise receiving a plurality of first data points, each data point of the first data points being represented in a floating-point representation. The methods further comprise converting the plurality of first data points into a corresponding plurality of second data points. Each of the second data points is represented in a dynamic fixed-point representation. The plurality of second data points may include: for each second data point, the sign component of the corresponding first data point, for each second data point, a dynamic fixed-point mantissa component, and one or more shared fraction components. At least two of the second data points share a value of a shared fraction component of the one or more shared fraction components. The methods further comprise performing integer computations during training of the machine learning model using the second data points.

Abstract: A method and system for pruning a neural network (NN) block of a neural network during training, wherein the NN block comprises: a convolution operation configured to convolve an input feature map with a plurality of filters, each filter including a plurality of weights, to generate a plurality of filter outputs each corresponding to a respective filter; an activation operation configured to generate, for each of the filter outputs, a respective non-linearized output; a scaling operation configured to scale the non-linearized output generated in respect of each filter by multiplying the non-linearized output with a mask function and a respective scaling factor that corresponds to the filter.