Abstract: A sparse video inference chip is designed to extract spatio-temporal features from videos for action classification and motion tracking. The core is a sparse video inference processor that implements recurrent neural network in three layers of processing. High sparsity is enforced in each layer of processing, reducing the complexity by two orders of magnitude and allowing all multiply-accumulates (MAC) to be replaced by select-accumulates (SA). The design is demonstrated in a 3.98 mm2 40 nm CMOS chip with an Open-RISC processor providing software-defined control and classification.

Abstract: An information processor is provided that includes an inference module configured to extract a subset of data from information in an input and a classification module configured to classify the information in the input based on the extracted data. The inference module includes a first plurality of convolvers acting in parallel to apply each of N1 convolution kernels to each of N2 portions of the input image in order to generate an interim sparse representation of the input and a second plurality of convolvers acting in parallel to apply each of N3 convolution kernels to each of N4 portions of the interim sparse representation to generate a final sparse representation containing the extracted data. In order to take advantage of sparsity in the interim sparse representation, N3 is greater than N4 to parallelize processing in a non-sparse dimension and/or the second plurality of convolvers comprise sparse convolvers.

Abstract: Systems and methods for providing visualizations of forecast weather in accordance with information received from a forecast database. A forecast engine generates analysis and forecast grids indicative of weather conditions which is stored at a weather data server. A data processor receives user inputs and converts the grids into data representative of predetermined objects within a scene. An element generator generates the objects as graphical elements, and a render receives the graphical elements and renders the scene. The system automatically generates a plurality of scenes as a visualization of weather over a predetermined period of time in accordance the grids for previous time periods through future time periods.