Apparatus and method for a compressed stack representation for hierarchical acceleration structures of arbitrary widths
a stack representation and hierarchical acceleration technology, applied in image memory management, instruments, computing, etc., can solve the problems of not having all rendered pixels accessible for computing a denoised portion of the image, and being too resource-intensive for real-time performan
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[0220]There are many types of neural networks; a simple type of neural network is a feedforward network. A feedforward network may be implemented as an acyclic graph in which the nodes are arranged in layers. Typically, a feedforward network topology includes an input layer and an output layer that are separated by at least one hidden layer. The hidden layer transforms input received by the input layer into a representation that is useful for generating output in the output layer. The network nodes are fully connected via edges to the nodes in adjacent layers, but there are no edges between nodes within each layer. Data received at the nodes of an input layer of a feedforward network are propagated (i.e., “fed forward”) to the nodes of the output layer via an activation function that calculates the states of the nodes of each successive layer in the network based on coefficients (“weights”) respectively associated with each of the edges connecting the layers. ...
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