Methods and apparatus for approximating numerical computations in deep neural networks
AssigneeIntel Corporation
InventorsBogdan Pasca, Hengyu Meng, Zhe Wang, Martin Langhammer, Haihao Shen
Life of this patent
Estimating the term…
Abstract
Methods, apparatus, and systems are disclosed for approximating numerical computations in deep neural networks. An example apparatus includes at least one memory, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to generate a lookup table based on input elements, index the lookup table using tensor values, the tensor values associated with an output index, and output a vector in destination numeric format based on the lookup table, the vector including output values in a floating-point representation.
Claims21
An apparatus, comprising:
- at least one memory;
- machine readable instructions;
- and
- programmable circuitry to at least one of instantiate or execute the machine readable instructions to:
- generate a lookup table based on input elements associated with a training phase of a deep neural network;
- index the lookup table using tensor values, the tensor values associated with an output index;
- and
- output a vector in destination numeric format based on the lookup table, the vector including output values in a floating-point representation.
A method, comprising:
- generating, by executing an instruction with at least one processor, a lookup table based on input elements associated with a training phase of a deep neural network;
- indexing, by executing an instruction with at least one processor, the lookup table using tensor values, the tensor values associated with an output index;
- and
- outputting, by executing an instruction with at least one processor, a vector in destination numeric format based on the lookup table, the vector including output values in a floating-point representation.
A non-transitory machine readable storage medium comprising instructions that, when executed, cause processor circuitry to at least:
- generate a lookup table based on input elements associated with a training phase of a deep neural network;
- index the lookup table using tensor values, the tensor values associated with an output index;
- and
- output a vector in destination numeric format based on the lookup table, the vector including output values in a floating-point representation.
-25. (canceled)
Dependencies are inferred from the claim text.