Abstract: The present disclosure discloses a system and method for distributed neural network training. The method includes: computing, by a plurality of heterogeneous computation units (HCUs) in a neural network processing system, a first plurality of gradients from a first plurality of samples; aggregating the first plurality of gradients to generate an aggregated gradient; computing, by the plurality of HCUs, a second plurality of gradients from a second plurality of samples; aggregating, at each of the plurality of HCUs, the aggregated gradient with a corresponding gradient of the second plurality of gradients to generate a local gradient update; and updating, at each of the plurality of HCUs, a local copy of a neural network with the local gradient update.

Abstract: Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Abstract: The present disclosure relates to a method and an apparatus for approximating non-linear function. In some embodiments, an exemplary processing unit includes: one or more registers for storing a lookup table (LUT) and one or more operation elements communicatively coupled with the one or more registers. The LUT includes a control state and a plurality of data entries. The one or more operation elements are configured to: receive an input operand; select one or more bits from the input operand; select a data entry from the plurality of data entries using the one or more bits; and determine an approximation value of a non-linear activation function for the input operand using the data entry.

Abstract: Methods and apparatuses for video transcoding based on spatial or temporal importance include: in response to receiving an encoded video bitstream, decoding a picture from the encoded video bitstream; determining a first level of spatial importance for a first region of a background of the picture based on an image segmentation technique; applying to the first region a first resolution-enhancement technique associated with the first level of spatial importance for increasing resolution of the first region by a scaling factor, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels; and encoding the first region using a video coding standard.

Abstract: The present disclosure relates to neural network processing apparatus and method. An exemplary neural network processing apparatus includes: a memory for storing data; an operation unit to perform an operation on a first batch of tensors; a statistics determination unit communicatively coupled to the operation unit and configured to receive an output from the operation unit and determine statistics from the output; and a normalization unit communicatively coupled to the memory and the statistics determination unit and configured to receive a second batch of tensors from the memory and the statistics from the statistics determination unit and to normalize the second batch of tensors using the statistics.

Abstract: Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Abstract: A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.

Abstract: A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.

Abstract: A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.

Abstract: A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.

Abstract: A video processing apparatus includes a programmable hardware encoder configured to execute an encoding process on a plurality of input video frames. The video processing apparatus further includes a controller coupled with the programmable hardware encoder. The controller is configured to execute a set of instructions to cause the video processing apparatus to: determine first information of the plurality of input video frames, and adjust the encoding process based on the first information.

Abstract: Methods and apparatuses for video transcoding based on spatial or temporal importance include: in response to receiving an encoded video bitstream, decoding a picture from the encoded video bitstream; determining a first level of spatial importance for a first region of a background of the picture based on an image segmentation technique; applying to the first region a first resolution-enhancement technique associated with the first level of spatial importance for increasing resolution of the first region by a scaling factor, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels; and encoding the first region using a video coding standard.

Abstract: A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.

Abstract: The present disclosure relates to a method and an apparatus for approximating non-linear function. In some embodiments, an exemplary processing unit includes: one or more registers for storing a lookup table (LUT) and one or more operation elements communicatively coupled with the one or more registers. The LUT includes a control state and a plurality of data entries. The one or more operation elements are configured to: receive an input operand; select one or more bits from the input operand; select a data entry from the plurality of data entries using the one or more bits; and determine an approximation value of a non-linear activation function for the input operand using the data entry.

Abstract: Methods and apparatuses for video processing based on spatial or temporal importance include: in response to receiving picture data of a picture of a video sequence, determining a level of semantic importance for the picture data, the picture data including a portion of the picture; and applying to the picture data a first resolution-enhancement technique associated with the level of semantic importance for increasing resolution of the picture data, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels.

Abstract: Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Abstract: A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.

Abstract: Embodiments of the present disclosure provides systems and methods for batch accessing. The system includes a plurality of buffers configured to store data; a plurality of processor cores that each have a corresponding buffer of the plurality of buffers; a buffer controller configured to generate instructions for performing a plurality of buffer transactions on at least some buffers of the plurality of buffers; and a plurality of data managers communicatively coupled to the buffer controller, each data manager is coupled to a corresponding buffer of the plurality of buffers and configured to execute a request for a buffer transaction at the corresponding buffer according to an instruction received from the buffer controller.

Abstract: The present disclosure relates to neural network processing apparatus and method. An exemplary neural network processing apparatus includes: a memory for storing data; an operation unit to perform an operation on a first batch of tensors; a statistics determination unit communicatively coupled to the operation unit and configured to receive an output from the operation unit and determine statistics from the output; and a normalization unit communicatively coupled to the memory and the statistics determination unit and configured to receive a second batch of tensors from the memory and the statistics from the statistics determination unit and to normalize the second batch of tensors using the statistics.

Abstract: The present disclosure discloses a system and method for distributed neural network training. The method includes: computing, by a plurality of heterogeneous computation units (HCUs) in a neural network processing system, a first plurality of gradients from a first plurality of samples; aggregating the first plurality of gradients to generate an aggregated gradient; computing, by the plurality of HCUs, a second plurality of gradients from a second plurality of samples; aggregating, at each of the plurality of HCUs, the aggregated gradient with a corresponding gradient of the second plurality of gradients to generate a local gradient update; and updating, at each of the plurality of HCUs, a local copy of a neural network with the local gradient update.