AI IN TELECOMMUNICATIONS

NVIDIA GPUs accelerate the actual training of channel autoencoders to speeds comparable to compiling programs for desktop processors in minutes, and sometimes even seconds. Once the model has been trained, it can then be deployed into a low-power, high performance embedded systems like the NVIDIA Jetson? TX2 or AGX  Xavier?. This creates a design flow where communications engineers can easily build optimized physical layers and then deploy them to embedded devices at the edge.

Accurately detecting the presence of humans is critical to a variety of applications. High-performance deep learning training makes it possible to create robust and generalizable models for objects, humans, animals, and machines for object detection over 5G using NVIDIA TensorRT. Maintaining real-time inference performance in production environments while preserving high accuracy is critical, whether in the data center or at the edge. Every percent increase in accuracy leads to exponential gains for organizations providing video-based services.