Critical node detection
AssigneeEntanglement, Inc.
InventorHaibo WANG
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Abstract
A computer-implemented method for optimizing neural networks by detecting critical and non-critical nodes is disclosed. The method involves obtaining a neural network comprising a plurality of nodes and their weighted connections. Critical nodes, which have a greater correlation to the network's output than non-critical nodes, are identified through a two-step detection process. The first critical node detection process identifies critical nodes based on weighted direct connections among the nodes. The second critical node detection process identifies critical nodes based on unweighted direct and indirect connections. The configuration of the neural network is then adjusted based on the identified critical and non-critical nodes to improve efficiency or reduce size.
Claims20
A method, comprising:
- obtaining a neural network comprising a plurality of nodes and weighted connections;
- identifying one or more critical nodes of the neural network from the plurality of nodes, wherein the one or more critical nodes have a greater correlation to an output of the neural network than other non-critical nodes, wherein the identifying comprises:
- executing a first critical node detection process to identify first critical nodes based on weighted direct connections among the plurality of nodes;
- executing a second critical node detection process to identify second critical nodes based on unweighted direct and indirect connections among the plurality of nodes;
- and
- adjusting a configuration of the neural network based on the identified critical nodes and non-critical nodes in the neural network for efficiency improvement or size reduction.
A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising:
- obtaining a neural network comprising a plurality of nodes and weighted connections;
- identifying critical nodes of the neural network from the plurality of nodes, wherein the critical nodes have a greater correlation to an output of the neural network than other non-critical nodes, wherein the identifying comprises:
- executing a first critical node detection process to identify first critical nodes based on weighted direct connections among the plurality of nodes;
- executing a second critical node detection process to identify second critical nodes based on unweighted direct and indirect connections among the plurality of nodes;
- and
- adjusting a configuration of the neural network based on the identified critical nodes and non-critical nodes in the neural network for efficiency improvement or size reduction.
A system comprising:
- at least one processor;
- and
- a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
- obtaining a neural network comprising a plurality of nodes and weighted connections;
- identifying critical nodes of the neural network from the plurality of nodes, wherein the critical nodes have a greater correlation to an output of the neural network than other non-critical nodes, wherein the identifying comprises:
- executing a first critical node detection process to identify first critical nodes based on weighted direct connections among the plurality of nodes;
- executing a second critical node detection process to identify second critical nodes based on unweighted direct and indirect connections among the plurality of nodes;
- and
- adjusting a configuration of the neural network based on the identified critical nodes and non-critical nodes in the neural network for efficiency improvement or size reduction.
Dependencies are inferred from the claim text.