Patent classifications
G09G2360/123
Regrouping of video data in host memory
Apparatus for data communications includes a host interface, which is configured to be connected to a bus of a host computer having a processor and a memory. Processing circuitry, which is coupled to the host interface, is configured to receive video data with respect to a sequence of pixels, the video data including data words of more than eight bits per pixel for at least one pixel component of the pixels, and to write the video data, via the host interface, to at least one buffer in the memory while justifying the video data in the memory so that the successive pixels in the sequence are byte-aligned in the at least one buffer.
Regrouping of video data in host memory
Apparatus for data communications includes a host interface, which is configured to be connected to a bus of a host computer having a processor and a memory. Processing circuitry, which is coupled to the host interface, is configured to receive video data with respect to a sequence of pixels, the video data including data words of more than eight bits per pixel for at least one pixel component of the pixels, and to write the video data, via the host interface, to at least one buffer in the memory while justifying the video data in the memory so that the successive pixels in the sequence are byte-aligned in the at least one buffer.
BIT PLANE DITHERING APPARATUS
A controller includes a frame memory configured to store an image frame, a frame memory controller coupled to the frame memory and configured to obtain image data from the image frame. The image data is associated with a color component of the image frame. The controller also includes a dither noise mask generator configured to provide dither noise masks according to dither noise levels for the image data, and a bit plane generator coupled to the frame memory controller and the dither noise mask generator and configured to generate bit planes based on the dither noise masks for the image data.
COMPUTE OPTIMIZATION MECHANISM FOR DEEP NEURAL NETWORKS
- Prasoonkumar Surti ,
- Narayan Srinivasa ,
- Feng Chen ,
- Joydeep Ray ,
- Ben J. Ashbaugh ,
- Nicolas C. Galoppo Von Borries ,
- Eriko Nurvitadhi ,
- Balaji Vembu ,
- Tsung-Han Lin ,
- Kamal Sinha ,
- Rajkishore Barik ,
- Sara S. Baghsorkhi ,
- Justin E. Gottschlich ,
- Altug Koker ,
- Nadathur Rajagopalan Satish ,
- Farshad Akhbari ,
- Dukhwan Kim ,
- Wenyin Fu ,
- Travis T. Schluessler ,
- Josh B. Mastronarde ,
- Linda L. Hurd ,
- John H. Feit ,
- Jeffery S. Boles ,
- Adam T. Lake ,
- Karthik Vaidyanathan ,
- Devan Burke ,
- Subramaniam Maiyuran ,
- Abhishek R. Appu
An apparatus to facilitate compute optimization is disclosed. The apparatus includes one or more processing units to provide a first set of shader operations associated with a shader stage of a graphics pipeline, a scheduler to schedule shader threads for processing, and a field-programmable gate array (FPGA) dynamically configured to provide a second set of shader operations associated with the shader stage of the graphics pipeline.
INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD AS WELL AS COMPUTER PROGRAM
An information processing apparatus is provided by which plural scenes are displayed simultaneously with low load.
The information processing apparatus that processes two or more image signals includes a division unit that decomposes the image signals for each color element, a selection unit that selects, from among the color elements of the two or more image signals, a color element of any one of the image signals for each color element, and an outputting unit that outputs the color elements of the image signals for each of predetermined regions. The predetermined regions are plural sub-frames into which one frame is divided in a time direction. The selection unit alternatively selects a color element from among the color elements of the two or more image signals in each of the sub-frames.
Apparatus and method for pixel data reordering
An apparatus includes a graphics pipeline and a pixel data reordering module. The graphics pipeline is configured to generate a plurality pieces of pixel data of a frame. The plurality pieces of pixel data of the frame are associated with a first order in which the plurality pieces of pixel data of the frame are to be provided to a display panel having an array of pixels. Each piece of pixel data of the frame corresponds to one pixel of the array of pixels. The array of pixels are divided into a plurality of groups of pixels. The pixel data reordering module is configured to cause the plurality pieces of pixel data of the frame to be obtained by the display panel in a second order. The second order is determined based on at least a manner in which the array of pixels are divided into the groups of pixels.
Compute optimization mechanism for deep neural networks
- Prasoonkumar Surti ,
- Narayan Srinivasa ,
- Feng Chen ,
- Joydeep Ray ,
- Ben J. Ashbaugh ,
- Nicolas C. Galoppo Von Borries ,
- Eriko Nurvitadhi ,
- Balaji Vembu ,
- Tsung-Han Lin ,
- Kamal Sinha ,
- Rajkishore Barik ,
- Sara S. Baghsorkhi ,
- Justin E. Gottschlich ,
- Altug Koker ,
- Nadathur Rajagopalan Satish ,
- Farshad Akhbari ,
- Dukhwan Kim ,
- Wenyin Fu ,
- Travis T. Schluessler ,
- Josh B. Mastronarde ,
- Linda L. Hurd ,
- John H. Feit ,
- Jeffery S. Boles ,
- Adam T. Lake ,
- Karthik Vaidyanathan ,
- Devan Burke ,
- Subramaniam Maiyuran ,
- Abhishek R. Appu
An apparatus to facilitate compute optimization is disclosed. The apparatus includes a memory device including a first integrated circuit (IC) including a plurality of memory channels and a second IC including a plurality of processing units, each coupled to a memory channel in the plurality of memory channels.
Compute optimization mechanism for deep neural networks
- Prasoonkumar Surti ,
- Narayan Srinivasa ,
- Feng Chen ,
- Joydeep Ray ,
- Ben J. Ashbaugh ,
- Nicolas C. Galoppo Von Borries ,
- Eriko Nurvitadhi ,
- Balaji Vembu ,
- Tsung-Han Lin ,
- Kamal Sinha ,
- Rajkishore Barik ,
- Sara S. Baghsorkhi ,
- Justin E. Gottschlich ,
- Altug Koker ,
- Nadathur Rajagopalan Satish ,
- Farshad Akhbari ,
- Dukhwan Kim ,
- Wenyin Fu ,
- Travis T. Schluessler ,
- Josh B. Mastronarde ,
- Linda L. Hurd ,
- John H. Feit ,
- Jeffery S. Boles ,
- Adam T. Lake ,
- Karthik Vaidyanathan ,
- Devan Burke ,
- Subramaniam Maiyuran ,
- Abhishek R. Appu
An apparatus to facilitate compute optimization is disclosed. The apparatus includes one or more processing units to provide a first set of shader operations associated with a shader stage of a graphics pipeline, a scheduler to schedule shader threads for processing, and a field-programmable gate array (FPGA) dynamically configured to provide a second set of shader operations associated with the shader stage of the graphics pipeline.
Regrouping of video data in host memory
Apparatus for data communications includes a host interface, which is configured to be connected to a bus of a host computer having a processor and a memory. Processing circuitry, which is coupled to the host interface, is configured to receive video data with respect to a sequence of pixels, the video data including data words of more than eight bits per pixel for at least one pixel component of the pixels, and to write the video data, via the host interface, to at least one buffer in the memory while justifying the video data in the memory so that the successive pixels in the sequence are byte-aligned in the at least one buffer.
Burst image data reading method and apparatus, electronic device, and readable storage medium
Disclosed are an image data reading method and apparatus, an electronic device, and a readable storage medium, relating to the technical field of LED image display. The image data reading method includes: storing image data in each row of image blocks into a number of v storage blocks, where each of the v storage blocks stores a number of h rows, each row of data including image data stored at a same position of each group in a same row of every image block; and sequentially outputting from each of the v storage blocks by: sequentially reading each row of the image data in a vertical order, and simultaneously outputting the image data stored at the same position.