NVIDIA announced the NVIDIA Metropolis™ intelligent video analytics platform today at the GPU Technology Conference (GTC) in San Jose, CA. Metropolis makes cities safer and smarter by applying deep learning to video streams for applications such as public safety, traffic management and resource optimization. More than 50 NVIDIA AI city partner companies are already providing
We love seeing all of the NVIDIA GPU-related tweets – here’s some that we came across this week: training machine learning models on CPU instead of GPU be like pic.twitter.com/4BjYSjSN9b — Simone Margaritelli (@evilsocket) May 3, 2017 GPU Heaven!! We received 2 of these babies. #TeslaGPU #nvidia #GPUpower #deeplearning #machinelearning #ArtificialIntelligence #HPC pic.twitter.com/sigR95WzBJ — Rolf
Whether you are using GPU clusters in the cloud or using your own data center to train deep neural networks, leading deep learning frameworks rely on NVIDIA’s Deep Learning SDK libraries to accelerate the massive amount of computation required to achieve high accuracy with deep learning. To learn more about the cuDNN and NCCL libraries
Proliferation of VR use-cases in gaming and professional visualization is putting increasing demands on realism and user immersion. This is particularly true in the gaming world, wherein absence of subtle environmental clues can make a significant difference to the gamer’s interest in the game and hence the game’s success.
Vivek Venugopalan, a staff research scientist at the United Technologies Research Center (UTRC) shares how they are using deep learning and GPUs to understand the life of an aircraft engine and predictive maintenance for elevators in high-rise buildings. “GPUs have helped us arrive at solutions quickly for computationally intensive challenges across all UTRC platforms, especially