In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

Systems, apparatuses, and methods for dynamic graphics processing unit (GPU) register allocation are disclosed. A GPU includes at least a plurality of compute units (CUs), a control unit, and a plurality of registers for each CU. If a new wavefront requests more registers than are currently available on the CU, the control unit spills registers associated with stack frames at the bottom of a stack since they will not likely be used in the near future. The control unit has complete flexibility determining how many registers to spill based on dynamic demands and can prefetch the upcoming necessary fills without software involvement. Effectively, the control unit manages the physical register file as a cache. This allows younger workgroups to be dynamically descheduled so that older workgroups can allocate additional registers when needed to ensure improved fairness and better forward progress guarantees.

An apparatus to facilitate neural network (NN) training is disclosed. The apparatus includes training logic to receive one or more network constraints and train the NN by automatically determining a best network layout and parameters based on the network constraints.

Disclosed embodiments relate to systems and methods for performing instructions specifying horizontal tile operations. In one example, a processor includes fetch circuitry to fetch an instruction specifying a horizontal tile operation, a location of a M by N source matrix comprising K groups of elements, and locations of K destinations, wherein each of the K groups of elements comprises the same number of elements, decode circuitry to decode the fetched instruction, and execution circuitry to respond to the decoded instruction by generating K results, each result being generated by performing the specified horizontal tile operation across every element of a corresponding group of the K groups, and writing each generated result to a corresponding location of the K specified destination locations.

A method for sorting of a vector in a processor is provided that includes performing, by the processor in response to a vector sort instruction, generating a control input vector for vector permutation logic comprised in the processor based on values in lanes of the vector and a sort order for the vector indicated by the vector sort instruction and storing the control input vector in a storage location.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

Methods, systems and apparatuses for performing walk operations of single instruction, multiple data (SIMD) instructions are disclosed. One method includes initiating, by a scheduler, a SIMD thread, where the scheduler is operative to schedule the SIMD thread. The method further includes fetching a plurality of instructions for the SIMD thread. The method further includes determining, by a thread arbiter, at least one instruction that is a walk instruction, where the walk instruction iterates a block of instructions for a subset of channels of the SIMD thread, where the walk instruction includes a walk size, and where the walk size is a number of channels in the subset of channels of the SIMD thread that are processed in a walk iteration in association with the walk instruction. The method further includes executing the walk instruction based on the walk size.

A processor includes: an address generating unit that, when an instruction decoded by a decoding unit is an instruction to execute arithmetic processing on a plurality of operand sets each including a plurality of operands that are objects of the arithmetic processing, in parallel a plurality of times, generates an address set corresponding to each of the operand sets of the arithmetic processing for each time, based on a certain address displacement with respect to the plurality of operands included in each of the operand sets; a plurality of instruction queues that hold the generated address sets corresponding to the respective operand sets, in correspondence to respective processing units; and a plurality of processing units that perform the arithmetic processing in parallel on the operand sets obtained based on the respective address sets outputted by the plurality of instruction queues.