Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, at training time, information relating to one or more tasks to be performed according to a training dataset relating to a processor including a graphics processor. The method may further include analyzing the information to determine one or more portions of hardware relating to the processor capable of supporting the one or more tasks, and configuring the hardware to pre-select the one or more portions to perform the one or more tasks, while other portions of the hardware remain available for other tasks.

Abstract: One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread (SIMT) architecture, the general-purpose graphics compute unit to simultaneously execute the first instruction and the second instruction, wherein the integer operation corresponds to a memory address calculation.

Abstract: A mechanism is described for facilitating barriers and synchronization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting thread groups relating to machine learning associated with one or more processing devices. The method may further include facilitating barrier synchronization of the thread groups across multiple dies such that each thread in a thread group is scheduled across a set of compute elements associated with the multiple dies, where each die represents a processing device of the one or more processing devices, the processing device including a graphics processor.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a memory device including a first integrated circuit (IC) including a plurality of memory channels and a second IC including a plurality of processing units, each coupled to a memory channel in the plurality of memory channels.

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: A mechanism is described for facilitating recognition, reidentification, and security in machine learning at autonomous machines. A method of embodiments, as described herein, includes facilitating a camera to detect one or more objects within a physical vicinity, the one or more objects including a person, and the physical vicinity including a house, where detecting includes capturing one or more images of one or more portions of a body of the person. The method may further include extracting body features based on the one or more portions of the body, comparing the extracted body features with feature vectors stored at a database, and building a classification model based on the extracted body features over a period of time to facilitate recognition or reidentification of the person independent of facial recognition of the person.

Abstract: An apparatus to facilitate compute optimization is disclosed.

Abstract: A mechanism is described for facilitating recognition, reidentification, and security in machine learning at autonomous machines. A method of embodiments, as described herein, includes facilitating a camera to detect one or more objects within a physical vicinity, the one or more objects including a person, and the physical vicinity including a house, where detecting includes capturing one or more images of one or more portions of a body of the person. The method may further include extracting body features based on the one or more portions of the body, comparing the extracted body features with feature vectors stored at a database, and building a classification model based on the extracted body features over a period of time to facilitate recognition or reidentification of the person independent of facial recognition of the person.

Abstract: Briefly, in accordance with one or more embodiments, an apparatus comprises a processor to monitor cache utilization of an application during execution of the application for a workload; and a memory to store cache utilization statistics responsive to the monitored cache utilization. The processor is to determine an optimal cache configuration for the application based at least in part on the cache utilization statistics for the workload such that a smallest amount of cache is turned on for subsequent executions of the workload by the application.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: Methods and apparatus relating to autonomous vehicle neural network optimization techniques are described. In an embodiment, the difference between a first training dataset to be used for a neural network and a second training dataset to be used for the neural network is detected. The second training dataset is authenticated in response to the detection of the difference. The neural network is used to assist in an autonomous vehicle/driving. Other embodiments are also disclosed and claimed.

Abstract: Briefly, in accordance with one or more embodiments, an apparatus comprises a processor to render data from an application to be displayed on a display panel, and a memory to store the compressed final display surface writes. The processor is to compress final display surface writes of the data to be displayed on the display panel in a format to be displayed on the display to allow a display engine coupled to the display to stream the compressed final display surface writes to the display.

Abstract: A control surface tracks an individual cacheline in the original surface for frequent data values. If so, control surface bits are set. When reading a cacheline from memory, first the control surface bits are read. If they happen to be set, then the original memory read is skipped altogether and instead the bits from the control surface provide the value for the entire cacheline.

Abstract: An embodiment of a graphics apparatus may include a context engine to determine contextual information, a recommendation engine communicatively coupled to the context engine to determine a recommendation based on the contextual information, and a configuration engine communicatively coupled to the recommendation engine to adjust a configuration of a graphics operation based on the recommendation. Other embodiments are disclosed and claimed.

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: In an example, an apparatus comprises a plurality of execution units, and a first general register file (GRF) communicatively couple to the plurality of execution units, wherein the first GRF is shared by the plurality of execution units. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to programmable power performance optimization for graphics cores are described. In one embodiment, the first frame of a scene is analyzed. It is then determined whether to optimize one or more operations, to be performed on one or more frames of the scene, based on the second frame of the scene and an idle status of one or more subsystems of a processor. And, one or more optimization operations are performed on a third frame of the scene based on the determination of whether to optimize the one or more operations. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to rotational graphics sub-slice and Execution Unit (EU) power down to improve power performance efficiency are described. In one embodiment, power-gating is rotated amongst single sub-slices within each slice of a plurality of slices based on an indication to reduce power consumption of a computational logic. The computational logic includes the plurality of slices and each of the plurality of slices includes a plurality of sub-slices to perform one or more computations. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to micro-architectural energy monitor event-assisted temperature sensing are described. In one embodiment, at least one of a plurality of slices of a computational logic or at least one of a plurality of sub-slices of the computational logic are powered down or powered up based on a comparison of a temperature value, that is determined based on one or more micro-architectural events, and a threshold value. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to rotational graphics sub-slice and Execution Unit (EU) power down to improve power performance efficiency are described. In one embodiment, power-gating is rotated amongst single sub-slices within each slice of a plurality of slices based on an indication to reduce power consumption of a computational logic. The computational logic includes the plurality of slices and each of the plurality of slices includes a plurality of sub-slices to perform one or more computations. Other embodiments are also disclosed and claimed.