Abstract: A processor-implemented method comprises processing a single level cost volume across multiple processing stages by varying a receptive field across each of the processing stages. The method also includes performing a learning-based correspondence estimation task based on the processing. The varying may include processing a different resolution of the cost volume at each processing stage while maintaining a same neighborhood sampling radius. The resolution may increase from a first processing stage to a later processing stage. The varying may also include varying a neighborhood sampling radius at each of the processing stages while maintaining a same resolution. The task may be optical flow estimation or stereo estimation.

Abstract: A computer-implemented method includes receiving a first input. The first input is interpolated based on a first shift along a first dimension and a second shift along a second dimension. A first output is generated based on the interpolated first input. The first output corresponds to a vectorized bilinear shift of the first input for use in place of grid sampling algorithms.

Abstract: A method for operating an artificial neural network (ANN) includes quantifying a reward for executing ANN tasks in a system having multiple processing cores. A set of processing cores of the multiple processing cores is allocated to execute each of the tasks based on the reward. The ANN tasks are executed concurrently according to the processing core allocation to operate the ANN.