Abstract: Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

Abstract: Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

Abstract: Certain aspects of the present disclosure provide techniques for adaptively executing machine learning models on a computing device. An example method generally includes receiving weight information for a machine learning model to be executed on a computing device. The received weight information is reduced into quantized weight information having a reduced bit size relative to the received weight information. First inferences using the machine learning model and the received weight information, and second inferences are performed using the machine learning model and the quantized weight information. Results of the first and second inferences are compared, it is determined that results of the second inferences are within a threshold performance level of results of the first inferences, and based on the determination, one or more subsequent inferences are performed using the machine learning model and the quantized weight information.

Abstract: Various embodiments include methods and devices for implementing decompression of compressed high dynamic ratio fields. Various embodiments may include receiving compressed first and second sets of data fields, decompressing the first and second compressed sets of data fields to generate first and second decompressed sets of data fields, receiving a mapping for mapping the first and second decompressed sets of data fields to a set of data units, aggregating the first and second decompressed sets of data fields using the mapping to generate a compression block comprising the set of data units.

Abstract: Various embodiments include methods and devices for implementing decompression of compressed high dynamic ratio fields. Various embodiments may include receiving compressed first and second sets of data fields, decompressing the first and second compressed sets of data fields to generate first and second decompressed sets of data fields, receiving a mapping for mapping the first and second decompressed sets of data fields to a set of data units, aggregating the first and second decompressed sets of data fields using the mapping to generate a compression block comprising the set of data units.

Abstract: Certain aspects of the present disclosure provide techniques for adaptively executing machine learning models on a computing device. An example method generally includes receiving weight information for a machine learning model to be executed on a computing device. The received weight information is reduced into quantized weight information having a reduced bit size relative to the received weight information. First inferences using the machine learning model and the received weight information, and second inferences are performed using the machine learning model and the quantized weight information. Results of the first and second inferences are compared, it is determined that results of the second inferences are within a threshold performance level of results of the first inferences, and based on the determination, one or more subsequent inferences are performed using the machine learning model and the quantized weight information.

Abstract: Various embodiments include methods and devices for implementing compression of high dynamic ratio fields. Various embodiments may include receiving a compression block having data units, receiving a mapping for the compression block, wherein the mapping is configured to map bits of each data unit to two or more data fields to generate a first set of data fields and a second set of data fields, compressing the first set of data fields together to generate a compressed first set of data fields, and compressing the second set of data fields together to generate a compressed second set of data fields.

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: Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

Abstract: Various embodiments include methods and devices for implementing compression of high dynamic ratio fields. Various embodiments may include receiving a compression block having data units, receiving a mapping for the compression block, wherein the mapping is configured to map bits of each data unit to two or more data fields to generate a first set of data fields and a second set of data fields, compressing the first set of data fields together to generate a compressed first set of data fields, and compressing the second set of data fields together to generate a compressed second set of data fields.

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: 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: 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 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: 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: 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: Techniques are described in which a device is configured to retrieve a metadata buffer for rendering a sub-frame of a set of sub-frames for a frame. A data block of a data buffer is configured to store image data for rendering the sub-frame. In response to determining, based on the metadata buffer for rendering the sub-frame, that the sub-frame includes a color pattern, fixed color value, or combination thereof, the device refrains from retrieving the image data from the data block of the data buffer and determines the image data for rendering the sub-frame based on the metadata buffer.

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: 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.