Fixed point integer implementations for neural networks
AssigneeIntel Corporation
InventorsPiotr Rozen, Georg Stemmer
Life of this patent
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Abstract
Techniques related to implementing neural networks for speech recognition systems are discussed. Such techniques may include processing a node of the neural network by determining a score for the node as a product of weights and inputs such that the weights are fixed point integer values, applying a correction to the score based on a correction value associated with at least one of the weights, and generating an output from the node based on the corrected score.
Claims20
A system to process a node of a neural network comprising:
- a memory to store weights associated with the node of the neural network;
- and
- one or more processors coupled to the memory, the one or more processors to:
- determine a score for the node of the neural network based on inputs to the node and the weights associated with the node, wherein the weights comprise a subset of first weights having associated correction values and a subset of second weights not having associated correction values;
- apply a plurality of corrections to the score responsive to the correction values associated with the subset of first weights, each correction comprising a product of the associated correction value and a corresponding input of the inputs to the node;
- and
- generate an output from the node based on the corrected score.
A method for processing a node of a neural network comprising:
- determining a score for the node of the neural network based on inputs to the node and weights associated with the node, wherein the weights comprise a subset of first weights having associated correction values and a subset of second weights not having associated correction values;
- applying a plurality of corrections to the score responsive to the correction values associated with the subset of first weights, each correction comprising a product of the associated correction value and a corresponding input of the inputs to the node;
- and
- generating an output from the node based on the corrected score.
At least one non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to process a node of a neural network by:
- determining a score for the node of the neural network based on inputs to the node and weights associated with the node, wherein the weights comprise a subset of first weights having associated correction values and a subset of second weights not having associated correction values;
- applying a plurality of corrections to the score responsive to the correction values associated with the subset of first weights, each correction comprising a product of the associated correction value and a corresponding input of the inputs to the node;
- and
- generating an output from the node based on the corrected score.
Dependencies are inferred from the claim text.