Abstract: Systems and methods for decoding are described. A decoder system is configured to retrieve, from the grain array stored within the on-chip memory, noise pixel data associated with at least one reconstructed video pixel. The decoder system is configured to apply noise data to the at least one reconstructed video pixel to generate at least one output video pixel. The noise data is determined based on the noise pixel data.

Abstract: Systems and techniques are provided for processing video data. For example, an apparatus may obtain one or more first sets of collocated motion vector data and one or more second sets of collocated motion vector data, associated with a respective first and second block of video data included in a current frame of video data. The apparatus may project the one or more first sets of collocated motion vector data into a first projected motion field associated with a first buffer and project the one or more second sets of collocated motion vector data into the first projected motion field associated with the first buffer. Based on projecting the one or more first sets and one or more second sets of collocated motion vector data, the apparatus may decode the first block of video data based on the first projected motion field associated with the first buffer.

Abstract: The disclosure relates to method and system for on-device inference in a deep neural network (DNN). The method comprises: determining whether one or more layers of the DNN satisfy one of a first, a second and a third condition, the one or more layers including one or more convolution layers and one or more resampling layers; performing the on-device inference based on the determination, wherein performing the on-device inference comprises at least one of: optimizing the one or more convolution layers in the one or more parallel branches based on the one or more layers of the DNN satisfying the first condition, optimizing the at least one of the resampling layers based on the one or more layers of the DNN satisfying the second condition, and modifying operation of the at least one of the resampling layers based on the one or more layers of the DNN satisfying the third condition.

Abstract: A data clustering device includes an input configured to receive a plurality of data points encoded in at least one signal and a hardware logic circuit configured to extract one or more features of the one or more data points from the at least one signal, create or update, based on the one or more features, one or more data clusters representing one or more of the data points, and encode at least one of the one or more data clusters in at least one output signal. The device further includes an output configured to provide the at least one output signal, for instance, to a processor, such as a processor for controlling a controlled system. The device can be further configured to split or merge the data cluster(s) based on a statistical distribution of the one or more data points in the respective data cluster.