Abstract:
An exemplary method for intelligent compression uses a foveated-compression approach. First, the location of a fixation point within an image frame is determined. Next, the image frame is sectored into two or more sectors such that one of the two or more sectors is designated as a fixation sector and the remaining sectors are designated as foveation sectors. A sector may be defined by one or more tiles within the image frame. The fixation sector includes the particular tile that contains the fixation point and is compressed according to a lossless compression algorithm. The foveation sectors are compressed according to lossy compression algorithms. As the locations of foveation sectors increase in angular distance from the location of the fixation sector, a compression factor may be increased.
Abstract:
An exemplary method for intelligent compression defines a threshold value for a key performance indicator. Based on the key performance indicator value, data blocks generated by a producer component may be scaled down to reduce power and/or bandwidth consumption when being compressed according to a lossless compression module. The compressed data blocks are then stored in a memory component along with metadata that signals the scaling factor used prior to compression. Consumer components later retrieving the compressed data blocks from the memory component may decompress the data blocks and upscale, if required, based on the scaling factor signaled by the metadata.
Abstract:
Various embodiments of methods and systems for managing compressed data transaction sizes in a system on a chip (“SoC”) in a portable computing device (“PCD”) are disclosed. Based on lengths of compressed data tiles associated in a group, wherein the compressed data tiles are comprised within a compressed image file, multiple compressed data tiles may be aggregated into a single, multi-tile transaction. A metadata file may be generated in association with the single multi-tile transaction to identify the transaction as a multi-tile transaction and provide offset data to distinguish data associated with the compressed data tiles. Using the metadata, embodiments of the solution may provide for random access and modification of the compressed data stored in association with a multi-tile transaction.
Abstract:
Systems, methods, and computer programs are disclosed for controlling memory frequency. One method comprises a first memory client generating a compressed data buffer and compression statistics related to the compressed data buffer. The compressed data buffer and the compression statistics are stored in a memory device. Based on the stored compression statistics, a frequency or voltage setting of the memory device is adjusted for enabling a second memory client to read the compressed data buffer.
Abstract:
A method and system for managing safe downtime of shared resources within a portable computing device are described. The method may include determining a tolerance for a downtime period for an unacceptable deadline miss element of the portable computing device. Next, the determined tolerance for the downtime period may be transmitted to quality-of-service (“QoS”) controller. The QoS controller may determine if the tolerance for the downtime period needs to be adjusted. The QoS controller may receive a downtime request from one or more shared resources of the portable computing device. The QoS controller may determine if the downtime request needs to be adjusted. Next, the QoS controller may select a downtime request for execution and then identify which one or more unacceptable deadline miss elements of the portable computing device that are impacted by the selected downtime request.
Abstract:
An exemplary method for intelligent compression uses a foveated-compression approach. First, the location of a fixation point within an image frame is determined. Next, the image frame is sectored into two or more sectors such that one of the two or more sectors is designated as a fixation sector and the remaining sectors are designated as foveation sectors. A sector may be defined by one or more tiles within the image frame. The fixation sector includes the particular tile that contains the fixation point and is compressed according to a lossless compression algorithm. The foveation sectors are compressed according to lossy compression algorithms. As the locations of foveation sectors increase in angular distance from the location of the fixation sector, a compression factor may be increased.
Abstract:
An exemplary method for intelligent compression defines a threshold value for a temperature reading generated by a temperature sensor. Data blocks received into the compression module are compressed according to either a first mode or a second mode, the selection of which is determined based on a comparison of the active level for the temperature reading to the defined threshold value. The first compression mode may be associated with a lossless compression algorithm while the second compression mode is associated with a lossy compression algorithm. Or, both the first compression mode and the second compression mode may be associated with a lossless compression algorithm, however, for the first compression mode the received data blocks are produced at a default high quality level setting while for the second compression mode the received data blocks are produced at a reduced quality level setting.
Abstract:
A method and system for dynamic control of shared memory resources within a portable computing device (“PCD”) are disclosed. A limit request of an unacceptable deadline miss (“UDM”) engine of the portable computing device may be determined with a limit request sensor within the UDM element. Next, a memory management unit modifies a shared memory resource arbitration policy in view of the limit request. By modifying the shared memory resource arbitration policy, the memory management unit may smartly allocate resources to service translation requests separately queued based on having emanated from either a flooding engine or a non-flooding engine.
Abstract:
A method and system for adjusting bandwidth within a portable computing device based on danger signals monitored from one on more elements of the portable computing device are disclosed. A danger level of an unacceptable deadline miss (“UDM”) element of the portable computing device may be determined with a danger level sensor within the UDM element. Next, a quality of service (“QoS”) controller may adjust a magnitude for one or more danger levels received based on the UDM element type that generated the danger level and based on a potential fault condition type associated with the particular danger level. The danger levels received from one UDM element may be mapped to at least one of another UDM element and a non-UDM element. A quality of service policy for each UDM element and non-UDM element may be mapped in accordance with the danger levels.