TEXTURE PROCESSOR BASED RAY TRACING ACCELERATION METHOD AND SYSTEM

    公开(公告)号:US20190197761A1

    公开(公告)日:2019-06-27

    申请号:US15853207

    申请日:2017-12-22

    CPC classification number: G06T15/06 G06T15/04

    Abstract: A texture processor based ray tracing accelerator method and system are described. The system includes a shader, texture processor (TP) and cache, which are interconnected. The TP includes a texture address unit (TA), a texture cache processor (TCP), a filter pipeline unit and a ray intersection engine. The shader sends a texture instruction which contains ray data and a pointer to a bounded volume hierarchy (BVH) node to the TA. The TCP uses an address provided by the TA to fetch BVH node data from the cache. The ray intersection engine performs ray-BVH node type intersection testing using the ray data and the BVH node data. The intersection testing results and indications for BVH traversal are returned to the shader via a texture data return path. The shader reviews the intersection results and the indications to decide how to traverse to the next BVH node.

    TAG ACCELERATOR FOR LOW LATENCY DRAM CACHE
    664.
    发明申请

    公开(公告)号:US20190196974A1

    公开(公告)日:2019-06-27

    申请号:US15855838

    申请日:2017-12-27

    Abstract: Systems, apparatuses, and methods for implementing a tag accelerator cache are disclosed. A system includes at least a data cache and a control unit coupled to the data cache via a memory controller. The control unit includes a tag accelerator cache (TAC) for caching tag blocks fetched from the data cache. The data cache is organized such that multiple tags are retrieved in a single access. This allows hiding the tag latency penalty for future accesses to neighboring tags and improves cache bandwidth. When a tag block is fetched from the data cache, the tag block is cached in the TAC. Memory requests received by the control unit first lookup the TAC before being forwarded to the data cache. Due to the presence of spatial locality in applications, the TAC can filter out a large percentage of tag accesses to the data cache, resulting in latency and bandwidth savings.

    Reduced setup time clock gating circuit

    公开(公告)号:US10331196B2

    公开(公告)日:2019-06-25

    申请号:US15626847

    申请日:2017-06-19

    Abstract: A system and method for providing efficient clock gating capability for functional units are described. A functional unit uses a clock gating circuit for power management. A setup time of a single device propagation delay is provided for a received enable signal. When each of a clock signal, the enable signal and a delayed clock signal is asserted, an evaluate node of the clock gating circuit is discharged. When each of the clock signal and a second clock signal is asserted and the enable signal is negated, the evaluate node is left floating for a duration equal to the hold time. Afterward, the devices in a delayed onset keeper are turned on and the evaluate node has a path to the power supply. When the clock signal is negated, the evaluate node is precharged.

    SCHEDULING MEMORY BANDWIDTH BASED ON QUALITY OF SERVICE FLOORBACKGROUND

    公开(公告)号:US20190190805A1

    公开(公告)日:2019-06-20

    申请号:US15849266

    申请日:2017-12-20

    Abstract: A system includes a multi-core processor that includes a scheduler. The multi-core processor communicates with a system memory and an operating system. The multi-core processor executes a first process and a second process. The system uses the scheduler to control a use of a memory bandwidth by the second process until a current use in a control cycle by the first process meets a first setpoint of use for the first process when the first setpoint is at or below a latency sensitive (LS) floor or a current use in the control cycle by the first process exceeds the LS floor when the first setpoint exceeds the LS floor.

    ADAPTIVE QUANTIZATION FOR NEURAL NETWORKS
    668.
    发明申请

    公开(公告)号:US20190188557A1

    公开(公告)日:2019-06-20

    申请号:US15849617

    申请日:2017-12-20

    CPC classification number: G06N3/063 G06N3/08

    Abstract: Methods, devices, systems, and instructions for adaptive quantization in an artificial neural network (ANN) calculate a distribution of ANN information; select a quantization function from a set of quantization functions based on the distribution; apply the quantization function to the ANN information to generate quantized ANN information; load the quantized ANN information into the ANN; and generate an output based on the quantized ANN information. Some examples recalculate the distribution of ANN information and reselect the quantization function from the set of quantization functions based on the resampled distribution if the output does not sufficiently correlate with a known correct output. In some examples, the ANN information includes a set of training data. In some examples, the ANN information includes a plurality of link weights.

    PSEUDO-RANDOM LOGICAL TO PHYSICAL CORE ASSIGNMENT AT BOOT FOR AGE AVERAGING

    公开(公告)号:US20190188001A1

    公开(公告)日:2019-06-20

    申请号:US15846781

    申请日:2017-12-19

    CPC classification number: G06F9/4408 G06F9/4403

    Abstract: A computing device includes a processor having a plurality of cores, a core translation component, and a core assignment component. The core translation component provides a set of registers, one register for each core of the multiple processor cores. The core assignment component includes components to provide a core index to each of the registers of the core translation component according to a core assignment scheme during processor initialization. Process instructions from an operating system are transferred to a respective core based on the core indices.

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