DLAP-301 Nano: Industrial AI applications with the Jetson engine

DLAP-301 Nano: Industrial AI applications with the Jetson engine

The intelligent control and monitoring of processes, machines and systems is increasingly based on large data quantities and complex algorithms, whose calculation requires specialised hardware and a high bandwidth for data communication. In particular, the provision and processing of image data from connected cameras poses the controllers used in the industrial environment challenges. They must be space-saving, robust and powerful and at the same time low-service and equipped with the essential interfaces. With the DLAP-301-Nano, Acceed now announces a new AI platform with an integrated Jetson nano engine tailored to these requirements.

Equipped with the Jetson nano module from Nvidia, the new fanless DLAP-301-Nano now introduced by Acceed is the ideal platform for starting with AI applications based on image data. The module has a 64-bit quad core CPU ARM Cortex-A57 and an integrated 128-CUDA core graphics processor (GPU) with Maxwell architecture. Combined with 4 GB 64-bit LPDDR4 memory, the module achieves 472 GFLOPS processor power. Nvidia specifies 25.6 GBit/s memory throughput. Here, depending on the mode activated, power consumption is only 5 to 10 W.

Eight RJ45 Ethernet interfaces with PoE serve for camera connection on the reverse side of the industrial casing with its width of a mere 21 cm. Moreover, two serial DB-9 sockets, three USB ports and a further GbE interface are available for data communication. Local graphics output is possible in high resolution via the HDMI-2.0 port on the front side. The enclosure, which is also accessible from the front side, accommodates a 2.5” SATA SSD for local data storage. With this, the DLAP-301-Nano can simultaneously be used as an autonomous NVR (network video recorder), which can also stream image data to the Internet if required.

With the Jetson nano module, the DLAP-301-Nano enables mature computer vision in real time and inferencing for several complex DNN (Deep Neural Network) models. For example, the control of multi-sensor autonomous robots could be realised or the integration of IoT devices with intelligent edge processing. Using ML frameworks, it should also be possible to train neuronal networks further directly.

The DLAP-301-Nano is developed for industrial application under demanding conditions and allows operation within an extended temperature range from -20 to +70 °C. The power supply is 12 VDC. The robust, full-metal chassis with its 210 x 170 x 55 mm is very compact.


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