Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: The present disclosure is directed to seamless access to a common physical disk in an AMP system without an external hypervisor, and includes one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations including instantiating, by a first instance, a second instance during a system upgrade, creating, in the first instance, a first disk abstraction for a block device of a physical disk, and attaching the block device under the first disk abstraction. The operations further include providing the second instance network-based access to the physical disk using the first disk abstraction of the first instance during the system upgrade.

Abstract: The present disclosure is directed to seamless access to a common physical disk in an AMP system without an external hypervisor, and includes one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations including instantiating, by a first instance, a second instance during a system upgrade, creating, in the first instance, a first disk abstraction for a block device of a physical disk, and attaching the block device under the first disk abstraction. The operations further include providing the second instance network-based access to the physical disk using the first disk abstraction of the first instance during the system upgrade.

Abstract: The present disclosure is directed to migrating logical volumes from a thick provisioned layout to a thin provisioned layout, and includes one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations comprising creating an abstraction layer on top of a logical volume in a storage device, the abstraction layer for accessing the logical volume, the logical volume one of a plurality of logical volumes in a volume group of the storage device; allocating a thin pool from remaining storage space in the volume group of the storage device; creating a snapshot of the logical volume; adding a thin virtual volume corresponding to the logical volume to the thin pool; and copying data from the snapshot to the thin virtual volume.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: The present disclosure is directed to migrating logical volumes from a thick provisioned layout to a thin provisioned layout, and includes one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components of the system to perform operations comprising creating an abstraction layer on top of a logical volume in a storage device, the abstraction layer for accessing the logical volume, the logical volume one of a plurality of logical volumes in a volume group of the storage device; allocating a thin pool from remaining storage space in the volume group of the storage device; creating a snapshot of the logical volume; adding a thin virtual volume corresponding to the logical volume to the thin pool; and copying data from the snapshot to the thin virtual volume.

Abstract: The present disclosure is directed to an in-memory communication infrastructure for an asymmetric multiprocessing system without an external hypervisor, and includes one or more processors and one or more computer-readable non-transitory storage media comprising instructions that, when executed by the one or more processors, cause one or more components to perform operations including identifying data for transmission from a first instance to a second instance, writing, by the first instance, the data into a first ring of a shared memory, the first ring configured as a first transmit ring for the first instance, sending an inter-processor interrupt to the second instance to alert the second instance of the data written into the first ring, reading, by the second instance, the data from the first ring, the first ring configured as a first receive ring for the second instance, and transmitting the data to an application of the second instance.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: Techniques and systems are provided for processing image data using one or more neural networks. For example, a patch of raw image data can be obtained. The patch can include a subset of pixels of a frame of raw image data, and the frame can be captured using one or more image sensors. The patch of raw image data includes a single color component for each pixel of the subset of pixels. At least one neural network can be applied to the patch of raw image data to determine a plurality of color component values for one or more pixels of the subset of pixels. A patch of output image data can then be generated based on application of the at least one neural network to the patch of raw image data. The patch of output image data includes a subset of pixels of a frame of output image data, and also includes the plurality of color component values for one or more pixels of the subset of pixels of the frame of output image data.

Abstract: Techniques and systems are provided for processing image data using one or more neural networks. For example, a patch of raw image data can be obtained. The patch can include a subset of pixels of a frame of raw image data, and the frame can be captured using one or more image sensors. The patch of raw image data includes a single color component for each pixel of the subset of pixels. At least one neural network can be applied to the patch of raw image data to determine a plurality of color component values for one or more pixels of the subset of pixels. A patch of output image data can then be generated based on application of the at least one neural network to the patch of raw image data. The patch of output image data includes a subset of pixels of a frame of output image data, and also includes the plurality of color component values for one or more pixels of the subset of pixels of the frame of output image data.