NVIDIA Aerial

NVIDIA Aerial is a set of SDKs that enable GPU-accelerated, software defined 5G wireless RANs. Today, NVIDIA Aerial provides two critical SDKs: cuVNF and cuBB.

The NVIDIA cuVNF SDK provides optimized input/output (IO) and packer processing whereby 5G packets are directly sent to GPU memory from GPUDirect capable network interface cards.

The NVIDIA cuBB SDK provides a fully-offloaded 5G Signal Processing pipeline (L1 5G Phy) which delivers unprecedented throughput and efficiency by keeping all physical layer processing within the GPU’s high-performance memory.

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NVIDIA CloudXR

The NVIDIA CloudXR SDK provides a way to stream graphics-intensive augmented reality (AR), virtual reality (VR) or mixed reality (MR), content often called XR over a radio signal (5G or Wifi) to one or more devices. The SDK also enables the streaming of OpenVR applications to a number of 5G-connected Android devices giving them greater access to high-powered servers as well as enabling access to graphics-intensive applications on relatively low-powered graphics hardware.

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CUDA

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

In GPU-accelerated applications, the sequential part of the workload runs on the CPU - which is optimized for single-threaded performance - while the compute intensive portion of the application runs on thousands of GPU cores in parallel.

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RAPIDS

The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

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TensorRT

NVIDIA TensorRT™ is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.



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TensorRT Inference Server

The TensorRT inference server is part of NVIDIA's TensorRT inferencing platform, providing a new software solution that expands on the utility of models and frameworks and improves utilization of both GPUs and CPUs.



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cuDNN

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.



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JetPack

NVIDIA JetPack SDK is the most comprehensive solution for building AI applications. Use NVIDIA SDK Manager to flash your Jetson developer kit with the latest OS image, install developer tools for both host computer and developer kit, and install the libraries and APIs, samples, and documentation needed to jumpstart your development environment.



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GPU-Based Design to Achieve 100µs Scheduling for 5G NR

The next-generation 5G New Radio (NR) cellular technology design supports extremely diverse use cases, such as broadband human-oriented communications and time-sensitive applications with ultra-low latency. 5G NR operates on a very broad frequency spectrum (from sub-GHz to 100 GHz).



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DeepSig: Deep Learning for Wireless Communications

Communications engineering strives to further improve metrics like throughput and interference robustness while simultaneously scaling to support the explosion of low-cost wireless devices. These often-competing needs makes system complexity intractable. Furthermore, algorithmic and hardware components are designed separately, then optimized, and integrated to form complete systems.



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NVIDIA CloudXR Delivers Low-Latency AR/VR Streaming Over 5G Networks to Any Device

This new software development kit helps businesses create and deliver high-quality, wireless AR and VR experiences from any application based on OpenVR, the broadly used VR hardware and software interface.



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5G at the Edge

Find out how Verizon is pairing 5G with an NVIDIA® Quadro RTX™ server on the network edge to create dynamic real-world applications for AR and VR. You'll also learn about the components necessary to create a GPU-based Edge Platform and how to offload GPU intensive processes to the mobile edge.

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Accelerate Your Operations in Telecom with AI

Using NVIDIA GPU-accelerated solutions, telecom service providers can speed time to data and insights for higher network quality, optimized resource planning, and better customer service. Learn how these transformations provide telecom innovators to use AI-optimized models for maximized productivity at the lowest TCO.

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Bring Real-Time AI to the Edge

AI is erupting at the edge. On factory floors. In stores. On city streets. In urgent care facilities. But it needs a cloud native, scalable, GPU-accelerated platform that can drive decisions in real time and allow every industry to deliver automated intelligence to the point of action. Learn how to deploy AI at the edge with the NVIDIA EGX platform.

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Unique Perspective of Re-Inventing Edge Computing Applications Harnessing GPU, AI and 5G

Edge computing still leverages the cloud as a crucial part of the system and many applications will harness the power of 5G features such as high speed multi-gigabit connections, huge amounts of data bandwidth, unprecedented amounts of capacity, super-low latency and ultra-reliable low latency communications (URLLC). Explore the opportunities of some of the interesting applications to help our community and environment.

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Secure and Efficient Image Recognition Applications on a 5G Network

Learn how to accelerate deep learning-based image recognition applications on the 5G network, the next-generation cellular mobile network from NTT Docomo. The 5G network will enable low latency and high data-rate telecommunication, making it suitable for deep learning applications that need to post and get large amounts of data via the network or need real-time inference.

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Accelerated Hyperscale Compute for AI at the Edge

Examine what the evolution of the 5G network means for telecommunications providers and examine how supporting the jump to 5G will require accelerated computing deployed in new patterns. You will learn how telecommunications companies can tackle the data tsunami that will emerge with 5G, explore the new intelligent edge, and share solutions to the challenges of 5G. IBM will also provide examples of application deployments to the edge and their use cases.


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NVIDIA DEEP LEARNING INSTITUTE

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Training is available as self-paced, online courses or in-person, instructor-led workshops.


DLI Course
Optimization and Deployment of TensorFlow Models with TensorRT

Learn how to optimize TensorFlow models to generate fast inference engines in the deployment stage.

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DLI Course
Signal Processing with DIGITS

Learn how to classify both image and image-like data using deep learning by converting radio frequency (RF) signals into images to detect a weak signal corrupted by noise.

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NVIDIA Telecommunications News


Supercomputing Platform for the Edge

The edge platform provides a high-performance, cloud-native platform that lets organizations harness rapidly streaming data from factory floors, manufacturing inspection lines and city streets to securely deliver next-generation AI, IoT and 5G-based services at scale, with low latency.

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Deepwave Digital Develops the First Deep Learning Spectrum Sensor for a 5G Network

Deepwave Digital is using the Artificial Intelligence Radio Transceiver (AIR-T) to create the first deep learning sensor for a 5G network. This network, the Citizens Broadband Radio Service (CBRS), will be the first autonomous spectrum sharing service provided by the telecommunications industry that leverages real time RF sensing.


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