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
Researchers from University of Edinburgh and Method Studios developed a real-time character control mechanism using deep learning that can help virtual characters walk, run and jump a little more naturally. “Data-driven motion synthesis using neural networks is attracting researchers in both the computer animation and machine learning communities thanks to its high scalability and runtime