Abstract:
A system and method for scalable video coding that includes base layer having lower resolution encoding, enhanced layer having higher resolution encoding and the data transferring between two layers. The system and method provides several methods to reduce bandwidth of inter-layer transfers while at the same time reducing memory requirements. Due to less memory access, the system clock frequency can be lowered so that system power consumption is lowered as well. The system avoids having prediction data from base layer to enhanced layer to be up-sampled for matching resolution in the enhanced layer as transferring up-sampled data can impose a big burden on memory bandwidth.
Abstract:
A system configured to perform scalable video encoding is provided. The system includes a memory; and a processing unit, wherein the processing unit is configured to: receive inter-layer data and a current picture, wherein the current picture has a base layer; upsample the inter-layer data to generate residual data and reconstruction data, wherein the inter-layer data includes a base mode flag; and encode the current picture to an enhanced layer using the upsampled inter-layer data based on a block type of the base layer and the base mode flag.
Abstract:
A system configured to perform scalable video encoding is provided. The system includes a memory; and a processing unit, wherein the processing unit is configured to: receive inter-layer data and a current picture, wherein the current picture has a base layer; upsample the inter-layer data to generate residual data and reconstruction data, wherein the inter-layer data includes a base mode flag; and encode the current picture to an enhanced layer using the upsampled inter-layer data based on a block type of the base layer and the base mode flag.
Abstract:
A system and method for scalable video coding that includes base layer having lower resolution encoding, enhanced layer having higher resolution encoding and the data transferring between two layers. The system and method provides several methods to reduce bandwidth of inter-layer transfers while at the same time reducing memory requirements. Due to less memory access, the system clock frequency can be lowered so that system power consumption is lowered as well. The system avoids having prediction data from base layer to enhanced layer to be up-sampled for matching resolution in the enhanced layer as transferring up-sampled data can impose a big burden on memory bandwidth.
Abstract:
A system and method for scalable video coding that includes base layer having lower resolution encoding, enhanced layer having higher resolution encoding and the data transferring between two layers. The system and method provides several methods to reduce bandwidth of inter-layer transfers while at the same time reducing memory requirements. Due to less memory access, the system clock frequency can be lowered so that system power consumption is lowered as well. The system avoids having prediction data from base layer to enhanced layer to be up-sampled for matching resolution in the enhanced layer as transferring up-sampled data can impose a big burden on memory bandwidth.
Abstract:
A system and method for scalable video coding that includes base layer having lower resolution encoding, enhanced layer having higher resolution encoding and the data transferring between two layers. The system and method provides several methods to reduce bandwidth of inter-layer transfers while at the same time reducing memory requirements. Due to less memory access, the system clock frequency can be lowered so that system power consumption is lowered as well. The system avoids having prediction data from base layer to enhanced layer to be up-sampled for matching resolution in the enhanced layer as transferring up-sampled data can impose a big burden on memory bandwidth.
Abstract:
A system and method for scalable video coding that includes base layer having lower resolution encoding, enhanced layer having higher resolution encoding and the data transferring between two layers. The system and method provides several methods to reduce bandwidth of inter-layer transfers while at the same time reducing memory requirements. Due to less memory access, the system clock frequency can be lowered so that system power consumption is lowered as well. The system avoids having prediction data from base layer to enhanced layer to be up-sampled for matching resolution in the enhanced layer as transferring up-sampled data can impose a big burden on memory bandwidth.
Abstract:
Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.
Abstract:
Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.
Abstract:
Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.