Abstract:
An apparatus to facilitate compute compression is disclosed. The apparatus includes a graphics processing unit including mapping logic to map a first block of integer pixel data to a compression block and compression logic to compress the compression block.
Abstract:
A mechanism is described for facilitating person tracking and data security in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, by a camera associated with one or more trackers, a person within a physical vicinity, where detecting includes capturing one or more images the person. The method may further include tracking, by the one or more trackers, the person based on the one or more images of the person, where tracking includes collect tracking data relating to the person. The method may further include selecting a tracker of the one or more trackers as a preferred tracker based on the tracking data.
Abstract:
An apparatus to facilitate compute optimization is disclosed. The apparatus includes sorting logic to sort processing threads into thread groups based on bit depth of floating point thread operations.
Abstract:
In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.
Abstract:
A processor includes a front end to receive an instruction, a decoder to decode the instruction, a set operations logic unit (SOLU) to execute the instruction, and a retirement unit to retire the instruction. The SOLU includes logic to store a first set of key-value pairs in a content-associative data structure, to receive a second set of key-value pairs, and to identify key-value pairs in the two sets with matching keys. The SOLU includes logic to add the second set of key-value pairs to the first set to produce an output set, and to apply an operation to the values of key-value pairs with matching keys, generating a single value for the matching key. The SOLU includes logic to produce an output set that includes key-value pairs from the first set with matching keys, and to discard key-value pairs from the first set with unique keys.
Abstract:
A processing device includes a sorting module, which adds to each of a plurality of elements a position value of a corresponding position in a register rest resulting in a plurality of transformed elements in corresponding positions. The plurality of elements include a plurality of bits. The sorting module compares each of the plurality of transformed elements to itself and to one another. The sorting module also assigns one of an enabled or disabled indicator to each of the plurality of the transformed elements based on the comparison. The sorting module further counts a number of the enabled indicators assigned to each of the plurality of the transformed elements to generate a sorted sequence of the plurality of elements.
Abstract:
Systems and methods to accelerate compression and decompression with a search unit implemented in the processor core. According to an embodiment, a search unit may be implemented to perform compression or decompression on an input stream of data. The search unit may use a look-up table to identify appropriate compression or decompression symbols. The look-up table may be populated with a table derived using the variable length coding symbols of a sequence of vertices to be compressed or extracted from a received data stream to be decompressed. A comparator and a finite state machine may be implemented in the search unit to facilitate traversal of the look-up table.
Abstract:
An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.
Abstract:
Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.
Abstract:
Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.