Abstract: One embodiment provides for a general-purpose graphics processing unit comprising a set of processing elements to execute one or more thread groups of a second kernel to be executed by the general-purpose graphics processor, an on-chip memory coupled to the set of processing elements, and a scheduler coupled with the set of processing elements, the scheduler to schedule the thread groups of the kernel to the set of processing elements, wherein the scheduler is to schedule a thread group of the second kernel to execute subsequent to a thread group of a first kernel, the thread group of the second kernel configured to access a region of the on-chip memory that contains data written by the thread group of the first kernel in response to a determination that the second kernel is dependent upon the first kernel.

Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Abstract: Systems, apparatuses and methods may provide for technology that selects an anti-aliasing mode for a vertex of a primitive based on a parameter associated with the vertex and generates a coverage mask based on the selected anti-aliasing mode. Additionally, one or more pixels corresponding to the vertex may be shaded based at least partly on the coverage mask, wherein the selected anti-aliasing mode varies across a plurality of vertices in the primitive.

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: An apparatus and method for dynamic provisioning, quality of service, and prioritization in a graphics processor. For example, one embodiment of an apparatus comprises a graphics processing unit (GPU) comprising a plurality of graphics processing resources; slice configuration hardware logic to logically subdivide the graphics processing resources into a plurality of slices; and slice allocation hardware logic to allocate a designated number of slices to each virtual machine (VM) of a plurality of VMs running in a virtualized execution environment, the slice allocation hardware logic to allocate different numbers of slices to different VMs based on graphics processing requirements and/or priorities of each of the VMs.

Abstract: Described herein is a graphics processor comprising a memory device and a graphics processing cluster coupled with the memory device. The graphics processing cluster includes a plurality of graphics multiprocessors interconnected via a data interconnect. A graphics multiprocessor includes circuitry configured to load a modular neural network including a plurality of subnetworks, each of the plurality of subnetworks trained to perform a computer vision operation on a separate subject.

Abstract: An apparatus and method are described for implementing memory management in a graphics processing system. For example, one embodiment of an apparatus comprises: a first plurality of graphics processing resources to execute graphics commands and process graphics data; a first memory management unit (MMU) to communicatively couple the first plurality of graphics processing resources to a system-level MMU to access a system memory; a second plurality of graphics processing resources to execute graphics commands and process graphics data; a second MMU to communicatively couple the second plurality of graphics processing resources to the first MMU; wherein the first MMU is configured as a master MMU having a direct connection to the system-level MMU and the second MMU comprises a slave MMU configured to send memory transactions to the first MMU, the first MMU either servicing a memory transaction or sending the memory transaction to the system-level MMU on behalf of the second MMU.

Abstract: An apparatus to facilitate thread scheduling is disclosed. The apparatus includes logic to store barrier usage data based on a magnitude of barrier messages in an application kernel and a scheduler to schedule execution of threads across a plurality of multiprocessors based on the barrier usage data.

Abstract: Bank aware thread scheduling and early dependency clearing techniques are described herein. In one example, bank aware thread scheduling involves arbitrating and scheduling threads based on the cache bank that is to be accessed by the instructions to avoiding bank conflicts. Early dependency clearing involves clearing dependencies for cache loads in a scoreboard before the data is loaded. In early dependency clearing for loads, delays in operation can be reduced by clearing dependencies before data is required from the cache.

Abstract: A cache streaming apparatus and method for machine learning. For example, one embodiment of an apparatus comprises: a plurality of compute units to perform machine learning operations; a cache subsystem comprising a hierarchy of cache levels, at least some of the cache levels shared by two or more of the plurality of compute units; and data streaming hardware logic to stream machine learning data in and out of the cache subsystem based on the machine learning operations, the data streaming hardware logic to load data into the cache subsystem from memory before the data is needed by a first portion of the machine learning operations and to ensure that results produced by the first portion of machine learning operations are maintained in the cache subsystem until used by a second portion of the machine learning operations.

Abstract: Apparatus and method for prefetching node data. For example, one embodiment of an apparatus comprises: ray tracing acceleration hardware to manage active ray tracing stack allocations within a traversal stack; and stack management hardware logic to issue a prefetch operation comprising an indication of bounding volume hierarchy (BVH) node data to be prefetched and an indication of a cache level, wherein responsive to performing the prefetch operation, the BVH node data is to be prefetched to the indicated cache level.

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: Methods and apparatus relating to scalar core integration in a graphics processor. In an example, an apparatus comprises a processor to receive a set of workload instructions for a graphics workload from a host complex, determine a first subset of operations in the set of operations that is suitable for execution by a scalar processor complex of the graphics processing device and a second subset of operations in the set of operations that is suitable for execution by a vector processor complex of the graphics processing device, assign the first subset of operations to the scalar processor complex for execution to generate a first set of outputs, assign the second subset of operations to the vector processor complex for execution to generate a second set of outputs. Other embodiments are also disclosed and claimed.

Abstract: An embodiment of a graphics apparatus may include a focus identifier to identify a focus area, and a color compressor to selectively compress color data based on the identified focus area. Another embodiment of a graphics apparatus may include a motion detector to detect motion of a real object, a motion predictor to predict a motion of the real object, and an object placer to place a virtual object relative to the real object based on the predicted motion of the real object. Another embodiment of a graphics apparatus may include a frame divider to divide a frame into viewports, a viewport prioritizer to prioritize the viewports, a renderer to render a viewport of the frame in order in accordance with the viewport priorities, and a viewport transmitter to transmit a completed rendered viewport. Other embodiments are disclosed and claimed.

Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.

Abstract: In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating fast data operations and for facilitating a finite state machine for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting input data to be used in computational tasks by a computation component of a processor including a graphics processor. The method may further include determining one or more frequently-used data values (FDVs) from the data, and pushing the one or more frequent data values to bypass the computational tasks.

Abstract: In an example, an apparatus comprises a plurality of execution units, and a first memory communicatively couple to the plurality of execution units, wherein the first shared memory is shared by the plurality of execution units and a copy engine to copy context state data from at least a first of the plurality of execution units to the first shared memory. Other embodiments are also disclosed and claimed.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Abstract: Methods and apparatus relating to techniques for avoiding cache lookup for cold cache. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to collect user information for a user of a data processing device, generate a user profile for the user of the data processing device from the user information, and set a power profile a processor in the data processing device using the user profile. Other embodiments are also disclosed and claimed.