Electronics and Telecommunications Research Institute

Jae-Bok Park

Seon-Tae Kim

Ji-Young Kwak

Kyung-Hee Lee

Chang-Sik Cho

Ik-Soo Shin

Jin-Wuk Seok

Duk-Kyun Woo

Hyun-Woo Cho

Hong-Soog Kim

Disclosed herein are a neural network model deployment method and apparatus for providing a deep learning service. The neural network model deployment method may include providing a specification wizard to a user, searching for and training a neural network based on a user requirement specification that is input through the specification wizard, generating a neural network template code based on the user requirement specification and the trained neural network, converting the trained neural network into a deployment neural network that is usable in a target device based on the user requirement specification, and deploying the deployment neural network to the target device.

Neural network model deployment method and apparatus for providing deep learning service

An apparatus and method for generating a neural network executable image are disclosed. The apparatus receives user requirements including a default neural network model and training result data for generating a neural network executable image required by a user, checks whether the default neural network model included in the user requirements is capable of being supported in a target system in which the neural network executable image is to be installed, converts the default neural network model into a neural network model executable in the target system, converts the training result data by reconfiguring the data format set of the training result data, and generates a neural network executable image by combining the converted neural network model and the converted training result data.

Apparatus and method for generating executable image of neural network

Disclosed herein are an apparatus and method for developing a neural network application. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program receives a target specification and an application specification including user requirements, searches for a neural network model corresponding to the target specification and the application specification in a database, builds an inference engine for performing a neural network operation used by the neural network model, and generates a target image for executing the neural network model to be suitable for a target device using the inference engine.

Apparatus and method for developing neural network applications

Disclosed herein are a location measurement method and apparatus. The apparatus includes a first grading unit, a first presumed line calculation unit, a second grading unit, a second presumed lined calculating unit, a presumed location calculation unit, and a final location calculation unit. The first grading unit determines the grade of a first RSSI. The first presumed line calculation unit calculates the range of the object from a first node based on the grade of the first RSSI. The second grading unit determines the grade of a second RSSI. The second presumed line calculating unit calculates the range of the object from a second node based on the grade of the second RSSI. The presumed location calculation unit calculates two presumed locations. The final location calculation unit determines one of the two presumed locations to be the final location of the object.

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Daejeon, South Korea

Jae-Bok Park