Perform deep learning inference on signals. Contribute to deepwavedigital/gr-wavelearner development by creating an account on GitHub.
Perform deep learning inference on signals. Contribute to deepwavedigital/gr-wavelearner development by creating an account on GitHub.
Build and install TensorFlow C++ API library. Contribute to FloopCZ/tensorflow_cc development by creating an account on GitHub.
New open source libraries from Nvidia provide GPU acceleration of data analytics an machine learning. Company claims 50x speed-ups over CPU-only implementations.
CUDA 10 supports the new Turing architecture, including added Tensor Core data types, CUDA graphs, and improved analysis tools
GTC Japan -- Fueling the growth of AI services worldwide, NVIDIA today launched an AI data center platform that delivers the industry’s most advanced inference acceleration for voice, ...
It only took three fscking hours of keynote to announce it – where's the GPU optimization for that?
In the last 20 years, the video gaming industry drove forward huge advances in GPUs (graphical processing units), used to do the matrix math needed for rendering graphics. The status of ...
Thanks to the CUDA architecture [1] developed by NVIDIA, developers can exploit GPUs’ parallel computing power to perform general computation without extra efforts. Our objective is to ...