Caching and clearing mechanism for deep convolutional neural networks

    公开(公告)号:US11558628B2

    公开(公告)日:2023-01-17

    申请号:US17549039

    申请日:2021-12-13

    Abstract: An apparatus includes circuitry configured to: partition an input tensor into one or more block tensors; partition at least one of the block tensors into one or more continuation bands, the one or more continuation bands being associated with a caching counter having a value; store the one or more continuation bands in a cache managed using a cache manager; retrieve, prior to a convolution or pooling operation on a current block tensor, the one or more continuation bands of a previous block tensor from the cache that are adjacent to a current block tensor; concatenate the retrieved continuation bands with the current block tensor; apply the convolution or pooling operation on the current block tensor after the concatenation; decrease the respective caching counter value of the retrieved continuation bands; and clear the continuation bands from the cache when its respective caching counter reaches a value of zero.

    Apparatus, a Method and a Computer Program for Video Coding and Decoding

    公开(公告)号:US20220141471A1

    公开(公告)日:2022-05-05

    申请号:US17575946

    申请日:2022-01-14

    Abstract: A method includes maintaining a set of parameters or weights derived through online learning for a neural net; transmitting an update of the parameters or weights to a decoder; deriving a first prediction block based on an output of the neural net using the parameters or weights; deriving a first encoded prediction error block through encoding a difference of the first prediction block and a first input block; encoding the first encoded prediction error block into a bitstream; deriving a reconstructed prediction error block based on the first encoded prediction error block; deriving a second prediction block based on an output of the neural net using the parameters or weights and the reconstructed prediction error block; deriving a second encoded prediction error block through encoding a difference of the second prediction block and a second input block; and encoding the second encoded prediction error block into a bitstream.

    Method for Real Time Texture Adaptation

    公开(公告)号:US20210209829A1

    公开(公告)日:2021-07-08

    申请号:US17134711

    申请日:2020-12-28

    Abstract: An apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: receive a scene description comprising data associated with a scene; place the data associated with the scene into data buffers and create command buffers; adapt the data placed within the data buffers and synchronize the data within the data buffers with information provided from local media or network media; signal information about the adaptation to update the command buffers that command a renderer; and render the scene using the data within the data buffers and the command buffers.

    An Apparatus, A Method and a Computer Program for Video Coding and Decoding

    公开(公告)号:US20210195206A1

    公开(公告)日:2021-06-24

    申请号:US16771115

    申请日:2018-12-03

    Abstract: A method comprising: deriving a first prediction block (608) at least partly based on an output of a neural net (602) using a first set of parameters; deriving a first encoded prediction error block (614-620) through encoding a difference of the first prediction block and a first input block; encoding (620) the first encoded prediction error block into a bitstream; deriving a first reconstructed prediction error block (624) from the first encoded prediction error block; deriving a training signal (628) from one or both of the first encoded prediction error block and/or the first reconstructed prediction error block (624); retraining (630) the neural net (602) with the training signal (628) to obtain a second set of parameters for the neural net (602); deriving a second prediction block (608) at least partly based on an output of the neural net using the second set of parameters; deriving a second encoded prediction error block (614-620) through encoding a difference of the second prediction block and a second input block; and encoding (620) the second encoded prediction error block into a bitstream. The invention relates to image or video encoding or decoding, especially by online training a neural network (602) that is in the prediction loop.

    Graph diffusion for structured pruning of neural networks

    公开(公告)号:US12242969B2

    公开(公告)日:2025-03-04

    申请号:US17354398

    申请日:2021-06-22

    Abstract: An apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: estimate an importance of parameters of a neural network based on a graph diffusion process over at least one layer of the neural network; determine the parameters of the neural network that are suitable for pruning or sparsification; remove neurons of the neural network to prune or sparsify the neural network; and provide at least one syntax element for signaling the pruned or sparsified neural network over a communication channel, wherein the at least one syntax element comprises at least one neural network representation syntax element.

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