This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This is the first post in the CUDA Refresher series, which has the goal of refreshing key concepts in CUDA, tools, and optimization for beginning or intermediate developers. Scientific discovery and business analytics drive an insatiable demand […]
CUDA Refresher: Reviewing the Origins of GPU Computing Read More +
NVIDIA’s Jetson Nano is the 2020 Vision Product of the Year Award Winner in the AI Processors category. NVIDIA’s Jetson Nano delivers the power of modern AI in the smallest supercomputer for embedded and IoT. Jetson Nano is a small form factor, power-efficient, low-cost and production-ready System on Module (SOM) and Developer Kit that opens
2020 Vision Product of the Year Award Winner Showcase: NVIDIA (AI Processors) Read More +
New NVIDIA A100 GPU Boosts AI Training and Inference up to 20x; NVIDIA’s First Elastic, Multi-Instance GPU Unifies Data Analytics, Training and Inference; Adopted by World’s Top Cloud Providers and Server Makers SANTA CLARA, Calif., May 14, 2020 (GLOBE NEWSWIRE) — NVIDIA today announced that the first GPU based on the NVIDIA® Ampere architecture, the
NVIDIA’s New Ampere Data Center GPU in Full Production Read More +
Ecosystem Expands with EGX A100 and EGX Jetson Xavier NX Supported by AI-Optimized, Cloud-Native, Secure Software to Power New Wave of 5G and Robotics Applications SANTA CLARA, Calif., May 14, 2020 (GLOBE NEWSWIRE) — NVIDIA today announced two powerful products for its EGX Edge AI platform — the EGX A100 for larger commercial off-the-shelf servers
Cloud-Native Support Comes to Entire Jetson Platform Lineup, Making It Easier to Build, Deploy and Manage AI at the Edge Thursday, May 14, 2020—GTC 2020—NVIDIA today announced availability of the NVIDIA® Jetson Xavier™ NX developer kit with cloud-native support — and the extension of this support to the entire NVIDIA Jetson™ edge computing lineup for
NVIDIA Releases Jetson Xavier NX Developer Kit with Cloud-Native Support Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Supervised training of deep neural networks is now a common method of creating AI applications. To achieve accurate AI for your application, you generally need a very large dataset especially if you create… Training with Custom Pretrained Models
Training with Custom Pretrained Models Using the NVIDIA Transfer Learning Toolkit Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This is an updated version of How to Speed Up Deep Learning Inference Using TensorRT. This version starts from a PyTorch model instead of the ONNX model, upgrades the sample application to use TensorRT 7, and replaces the
Speeding Up Deep Learning Inference Using TensorRT Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The Virtual Reality (VR) industry is in the midst of a new hardware cycle – higher resolution headsets and better optics being the key focus points for the device manufacturers. Similarly on the software front, there has been
NVIDIA VRSS, a Zero-Effort Way to Improve Your VR Image Quality Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Every year, clever researchers introduce ever more complex and interesting deep learning models to the world. There is of course a big difference between a model that works as a nice demo in isolation and a model that
Accelerating WinML and NVIDIA Tensor Cores Read More +
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.
Speeding Up Deep Learning Inference Using TensorFlow, ONNX, and TensorRT Read More +
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.
Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596
