Abstract: Methods and systems for obtaining data having a first format, converting the data to a second format, storing the converted data in memory accessible by at least one parallel processing unit, and processing the converted data stored in the memory using the at least one parallel processing unit.

Abstract: Apparatuses, systems, and techniques to partition neural networks. In at least one embodiment, one or more circuits are to cause one or more neural networks to be dynamically partitioned based, at least in part, on one or more performance metrics of the one or more neural networks.

Abstract: Apparatuses, systems, and techniques to allocate computing resources to perform inferences. In at least one embodiment, one or more neural networks cause computing resources to be identified based, at least in part, on performance requirements of one or more neural networks to perform inferences.

Abstract: Described is a system for ordering images. The system receives a plurality of images. Image features are extracted from each image. A set of all possible image pairs are generated for all images. A similarity metric with weights is generated between the images in each image pair in the set, with a net similarity metric thereafter generated by combining the similarity metrics. The images are then ordered according to the net similarity metrics to generate a computer-ordered set of images. The computer-ordered set of images is then displayed to the user, which allows the user to re-order the images to generate a user-ordered set of images. The weights are then optimized to minimize the distance between the computer-ordered set of images and the user-ordered set of images. The similarity metrics are then re-weighted, with the images thereafter being re-ordered according to the new metrics.