Electronics and Telecommunications Research Institute

Kyung-Hee Lee

Seon-Tae Kim

Ji-Young Kwak

Jae-Bok Park

Chang-Sik Cho

Hyun-Woo Cho

Ik-Soo Shin

Do-Hyung Kim

Jae-Ho Lee

Jin-Wuk Seok

Cheol Ryu

Hyung-Seok Lee

Hong-Soog Kim

Seok-Jin Yoon

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 is an apparatus for controlling an embedded device, through which the embedded device may be controlled in a host computer in real time. The apparatus for controlling an embedded device includes a host computer connected with a monitor, a mouse, and a keyboard; an embedded device; and a connection module for connecting the host computer with the embedded device such that a first task window for controlling the host computer and a second task window for controlling the embedded device are displayed on the monitor.

Apparatus and method for controlling an embedded device with a host computer in real time

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Daejeon, South Korea

Kyung-Hee Lee