Image processing with Jetson TX2 and Nvidia GPU
The EOS-J integrates Jetson TX2 chip and optionally four GigE or USB 3.0 interfaces for connecting cameras or frame grabbers
By: Acceed GmbH
Compared with the so often preferred cloud computing, edge computing is often the more efficient architecture in industrial environments, because the AI decisions must be taken directly at the machine, the robot or the autonomous vehicle, in other words "on the edge". Dependent on the intended use, data communication with the cloud is less appropriate. Current development trends such as autonomous driving and robotics applications demonstrate this clearly for the topic of vision computing. In both cases, "seeing" and "recognising"
The two models for the new vision system EOS-J now introduced by Acceed only differ with their respective four type GigE or USB 3.0 camera ports. An integrated Jetson TX2 chip is the core element of the EOS-J, which forms a compact system together with the cameras or frame grabbers and the 32 isolated IO interfaces in accordance with industrial requirements, developed for use in edge computing.
The prerequisite for vision computing is the fast, efficient processing of image data with a correspondingly highly performant graphics processor (GPU). The EOD-J is equipped with a Quadro GPU with pascal architecture from Nvidia. The Cortex-A57 processors from ARM 256 CUDA cores support robust deep learning and inference applications. Therefore, the system is prepared for the requirements as, for example, formulated for error inspection or object classification in manufacturing environments.
The development of application-
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