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US 12,412,232 B2Granted
Dynamic precision management for integer deep learning primitives
AssigneeIntel Corporation
InventorsNaveen K. Mellempudi, Dheevatsa Mudigere, Dipankar Das, Srinivas Sridharan
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Abstract
One embodiment provides for a graphics processing unit to perform computations associated with a neural network, the graphics processing unit comprising a hardware processing unit having a dynamic precision fixed-point unit that is configurable to convert elements of a floating-point tensor to convert the floating-point tensor into a fixed-point tensor.
Claims26
A graphics processing unit comprising:
- an interface coupled to an interconnect fabric;
- and
- a graphics core cluster including a plurality of multiprocessors that are interconnected via a data interconnect, a multiprocessor of the plurality of multiprocessors including circuitry configured to:
- receive an input tensor associated with an operation to be performed via the graphics core cluster, the input tensor including data elements in a floating-point format;
- divide the data elements of the input tensor into multiple blocks of data elements;
- determine a shared exponent for each of the multiple blocks of data elements;
- generate metadata for the multiple blocks to indicate the shared exponent for each of the multiple blocks of data elements;
- perform a compute operation on input including at least one of the multiple blocks of data elements and the metadata;
- and
- generate an output tensor via the compute operation.
7 more elements
A method comprising:
- receiving an input tensor associated with an operation to be performed via a graphics core cluster including a plurality of multiprocessors that are interconnected via a data interconnect, the input tensor including data elements in a floating-point format;
- dividing the data elements of the input tensor into multiple blocks of data elements;
- determining a shared exponent for each of the multiple blocks of data elements;
- generating metadata for the multiple blocks to indicate the shared exponent for each of the multiple blocks of data elements;
- performing a compute operation on input including at least one of the multiple blocks of data elements and the metadata;
- and
- generating an output tensor via the compute operation.
4 more elements
A graphics processing system comprising:
- a memory device;
- and
- a graphics core cluster coupled with the memory device, the graphics core cluster including a plurality of multiprocessors that are interconnected via a data interconnect, a multiprocessor of the plurality of multiprocessors including circuitry configured to:
- receive an input tensor associated with an operation to be performed via the graphics core cluster, the input tensor including data elements in a floating-point format;
- divide the data elements of the input tensor into multiple blocks of data elements;
- determine a shared exponent for each of the multiple blocks of data elements;
- generate metadata for the multiple blocks to indicate the shared exponent for each of the multiple blocks of data elements;
- perform a compute operation on input including at least one of the multiple blocks of data elements and the metadata;
- and
- generate an output tensor via the compute operation.
7 more elements
A non-transitory machine-readable medium having code stored thereon which, when executed by a machine, causes the machine to perform operations, comprising:
- receiving an input tensor associated with an operation to be performed by a processing circuit comprising one or more field programmable gate arrays (FPGAs), programmable processors, or a graphics core cluster, the graphics core cluster including a plurality of multiprocessors that are interconnected via a data interconnect, the input tensor including data elements in a floating-point format;
- dividing the data elements of the input tensor into multiple blocks of data elements;
- determining a plurality of exponents, each exponent of the plurality of exponents shared by data elements of a corresponding block of data elements;
- generating metadata for the multiple blocks of data elements, the metadata indicating the plurality of exponents;
- performing compute operations based on the input tensor, including a compute operation based on at least one of the multiple blocks of data elements and the metadata;
- and
- generating an output tensor from the compute operation.
4 more elements
Dependencies are inferred from the claim text.