Back to results
US 2026/0093771 A1Published application
Efficient one-dimensional (1d) convolution support on two-dimensional (2d) convolution engines
AssigneeQUALCOMM Incorporated
InventorHaoping XU
Life of this patent
Estimating the term…
Abstract
A processor-implemented method for efficient convolution operations includes receiving, by an artificial neural network, an input. The input is mapped to a multiple row input based on a processing capacity of a compute device. The compute device performs concurrent convolution operations according to the multiple row input. An output is generated based on the concurrent convolution operations.
Claims20
An apparatus, comprising:
- at least one memory;
- and
- at least one processor coupled to the at least one memory, the at least one processor configured to:
- receive, by an artificial neural network, an input;
- map the input to a multiple row input based at least in part on a processing capacity of a compute device;
- perform, by the compute device, concurrent convolution operations according to the multiple row input;
- and
- generate an output based on the concurrent convolution operations.
5 more elements
A processor-implemented method performed by one or more processors, the processor-implemented method comprising:
- receiving, by an artificial neural network, an input;
- mapping the input to a multiple row input based at least in part on a processing capacity of a compute device;
- performing, by the compute device, concurrent convolution operations according to the multiple row input;
- and
- generating an output based on the concurrent convolution operations.
2 more elements
An apparatus, comprising:
- means for receiving, by an artificial neural network, an input;
- means for mapping the input to a multiple row input based at least in part on a processing capacity of a compute device;
- means for performing, by the compute device, concurrent convolution operations according to the multiple row input;
- and
- means for generating an output based on the concurrent convolution operations.
2 more elements
Dependencies are inferred from the claim text.