Abstract: A computer implemented method, a system and a non-transitory storage medium are provided for efficiently processing probability distributions in a neural network using a probabilistic programming language. Probability distribution data structures are provided, as well as corresponding distribution-APIs. Each probability distribution data structure comprises an identification of a distribution type and a list of parameters representative of said distribution type. The distribution data structures correspond to parametric representations of the probability distributions. The distribution data structures are inputted as distribution tensors into the layers, processed within the layer and outputted as modified tensors to downstream layers of the neural network. If needed, distribution class objects can be assembled and dissembled within the layers, but distribution tensors are still inputted or outputted by the layers, thereby limiting dataflow in between the layers of the neural network.

Abstract: A method for accelerating a convolution operation includes receiving from an I/O interface, a first data set and a second data set. Transforming the first data set into a first converted data set, the first converted data set having the first format. Transforming the second data set into a second converted data set, the second converted data set having the second format. Loading into a convolution functional unit, the first converted data set and the second converted data set, where the convolution functional unit is configured to receive a first data in a first format, to receive a second data in a second format, and to output a third data in a third format. Receiving, by the task scheduler from the convolution functional unit, a result in the third format.