STORING NEURAL NETWORKS AND WEIGHTS FOR NEURAL NETWORKS
First Claim
1. A method, comprising:
- storing a plurality of weights of a neural network comprising a plurality of nodes and a plurality of connections between the plurality of nodes, wherein;
each weight of at least some of the plurality of weights is associated with a connection of the plurality of connections; and
the neural network comprises a binarized neural network;
receiving input data to be processed by the neural network;
determining whether a set of weights of the plurality of weights comprises one or more errors; and
selectively refraining from using the set of weights to process the input data using the neural network in response to determining that the set of weights comprises the one or more errors.
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Abstract
Systems and methods are disclosed for storing neural networks and weights for neural networks. In some implementations, a method is provided. The method includes storing a plurality of weights of a neural network comprising a plurality of nodes and a plurality of connections between the plurality of nodes. Each weight of the plurality of weights is associated with a connection of the plurality of connections. The neural network comprises a binarized neural network. The method also includes receiving input data to be processed by the neural network. The method further includes determining whether a set of weights of the plurality of weights comprises one or more errors. The method further includes refraining from using the set of weights to process the input data using the neural network in response to determining that the set of weights comprises the one or more errors.
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Citations
20 Claims
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1. A method, comprising:
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storing a plurality of weights of a neural network comprising a plurality of nodes and a plurality of connections between the plurality of nodes, wherein; each weight of at least some of the plurality of weights is associated with a connection of the plurality of connections; and the neural network comprises a binarized neural network; receiving input data to be processed by the neural network; determining whether a set of weights of the plurality of weights comprises one or more errors; and selectively refraining from using the set of weights to process the input data using the neural network in response to determining that the set of weights comprises the one or more errors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A device, comprising:
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a network interface; and a processing device configured to; receive, via the network interface, a plurality of weights of a neural network, wherein; the neural network comprises a plurality of nodes and a plurality of connections between the plurality of nodes; each weight of at least some of the plurality of weights is associated with a connection of the plurality of connections; and each weight of at least some of the plurality of weights comprises one of two values; determine whether a set of weights of the plurality of weights comprises one or more errors; and selectively refrain from using the set of weights to process input data using the neural network in response to determining that the set of weights comprises the one or more errors. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. An apparatus, comprising:
a processing device configured to; store a plurality of weights of a neural network comprising a plurality of nodes and a plurality of connections between the plurality of nodes, wherein; each weight of the plurality of weights is associated with a connection of the plurality of connections; and the neural network comprises a binarized neural network; determine whether a set of weights of the plurality of weights comprises one or more errors based on a parity check, wherein; the set of weights comprises multiple weights; and the multiple weights share the parity check; and selectively refrain from using the set of weights to process input data using the neural network in response to determining that the set of weights comprises the one or more errors.
Specification