NVIDIA

Speeding Up Deep Learning Inference Using TensorFlow, ONNX, and TensorRT

Algorithms, Articles, Automotive, NVIDIA, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Starting with TensorRT 7.0,  the Universal Framework Format (UFF) is being deprecated. In this post, you learn how to deploy TensorFlow trained deep learning models using the new TensorFlow-ONNX-TensorRT workflow. Figure 1 shows the high-level workflow of TensorRT. […]

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Learning to Rank with XGBoost and GPU

Algorithms, Articles, NVIDIA, Processors, Software, Tools /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. XGBoost is a widely used machine learning library, which uses gradient boosting techniques to incrementally build a better model during the training phase by combining multiple weak models. Weak models are generated by computing the gradient descent using

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Laser Focused: How Multi-View LidarNet Presents Rich Perspective for Self-Driving Cars

Automotive, Blog Posts, NVIDIA /

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Deep neural network takes a two-stage approach to address lidar processing challenges. Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how

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Building a Real-time Redaction App Using NVIDIA DeepStream, Part 2: Deployment

Articles, NVIDIA /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This post is the second in a series (Part 1) that addresses the challenges of training an accurate deep learning model using a large public dataset and deploying the model on the edge for real-time inference using NVIDIA

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Building a Real-time Redaction App Using NVIDIA DeepStream, Part 1: Training

Articles, NVIDIA /

This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Some of the biggest challenges in deploying an AI-based application are the accuracy of the model and being able to extract insights in real time. There’s a trade-off between accuracy and inference throughput. Making the model more accurate

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NVIDIA Introduces DRIVE AGX Orin — Advanced, Software-Defined Platform for Autonomous Machines

News, NVIDIA /

Tuesday, December 17, 2019 — NVIDIA today introduced NVIDIA DRIVE AGX Orin™, a highly advanced software-defined platform for autonomous vehicles and robots. The platform is powered by a new system-on-a-chip (SoC) called Orin, which consists of 17 billion transistors and is the result of four years of R&D investment. The Orin SoC integrates NVIDIA’s next-generation GPU

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Didi Chuxing Teams with NVIDIA for Autonomous Driving and Cloud Computing

News, NVIDIA /

Tuesday, December 17, 2019 — NVIDIA and Didi Chuxing (DiDi), the world’s leading mobile transportation platform, today announced that DiDi will leverage NVIDIA GPUs and AI technology to develop autonomous driving and cloud computing solutions. DiDi will use NVIDIA® GPUs in the data center for training machine learning algorithms and NVIDIA DRIVE™ for inference on

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NVIDIA Provides Transportation Industry Access to Its Deep Neural Networks for Autonomous Vehicles

News, NVIDIA /

Developers Also Gain Access to NVIDIA Advanced Learning Tools to Leverage DNNs Across Multiple Datasets While Preserving Data Privacy Tuesday, December 17, 2019 — NVIDIA today announced that it will provide the transportation industry with access to its NVIDIA DRIVE™ deep neural networks (DNNs) for autonomous vehicle development on the Developers Also Gain Access to

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NVIDIA Provides U.S. Postal Service AI Technology to Improve Delivery Service

News, NVIDIA /

Advanced AI System to Process Package Data 10x Faster with Higher Accuracy Tuesday, November 5, 2019 — GTC DC — NVIDIA today announced that the United States Postal Service – the world’s largest postal service, with 485 million mail pieces processed and delivered daily – is adopting end-to-end AI technology from NVIDIA to improve its package

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Int4 Precision for AI Inference

Blog Posts, NVIDIA /

This blog post was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. If there’s one constant in AI and deep learning, it’s never-ending optimization to wring every possible bit of performance out of a given platform. Many inference applications benefit from reduced precision, whether it’s mixed precision for recurrent

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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