Method and system for implementing a variable accuracy neural network
AssigneeSamsung Electronics Co., Ltd.
InventorsStefanos Laskaridis, Hyeji Kim, Stylianos Venieris
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Abstract
Disclosed is an electronic apparatus. The electronic apparatus includes a memory storing at least one instruction, and a processor coupled to the memory and configured to control the electronic apparatus, the processor configured to identify one of a plurality of exit points included in a neural network based on at least one constraint in at least one of processing or the electronic apparatus, process the input data via the neural network and obtain processing results output from the identified exit point as output data.
Claims17
A method for controlling an electronic apparatus, the method comprising:
- receiving input data;
- identifying one of a plurality of exit points included in a neural network based on a time taken to reach each exit point to provide a processing result and based on a memory capacity of the electronic apparatus, the neural network providing progressive hierarchical inference by having at least a coarse-grained domain and a fine-grained domain, and coarse-grained results output from exit points in the coarse-grained domain are further processed in the fine-grained domain to obtain fine-grained results;
- processing the input data via the neural network;
- obtaining processing results output from the identified exit point as output data;
- and
- using knowledge distillation, jointly training a plurality of classifiers corresponding to the plurality of exit points.
An electronic apparatus comprising:
- a memory storing at least one instruction;
- and
- a processor coupled to the memory and configured to control the electronic apparatus,
- wherein the processor is configured to:
- identify one of a plurality of exit points included in a neural network based on a time taken to reach each exit point to provide a processing result and based on a memory capacity of the electronic apparatus, the neural network providing progressive hierarchical inference by having at least a coarse-grained domain and a fine-grained domain, and coarse-grained results output from exit points in the coarse-grained domain are further processed in the fine-grained domain to obtain fine-grained results;
- process the input data via the neural network;
- obtain processing results output from the identified exit point as output data;
- and
- jointly train, using knowledge distillation, a plurality of classifiers corresponding to the plurality of exit points.
Dependencies are inferred from the claim text.