Bend by default executes code on CPU and GPU in parallel with Python-like syntax, making it a great choice for developers getting started with GPU development.
NVIDIA’s Quantum Cloud and CUDA-Q is spearheading the quantum revolution with its hybrid approach.
“I am encouraged with the progress that we’re making on hardware and software and certainly with the customer set,” said Lisa Su.
Jensen Huang’s NTU speech highlights NVIDIA’s resilience and future-thinking in spite of the company reaching the brink of failure thrice in three decades
The 16GB system-on-module is smaller and can be used in lower-power autonomous machines.
The new updates will provide powerful HPC solutions to scientific discovery
Building onto this architecture, the team has leveraged CUDA’s massive scale and added a new tier of hierarchy called the thread lock cluster.
Triton delivers substantial ease-of-use benefits over coding in CUDA.
A comparative analysis of open-source deep learning optimization libraries DeepSpeed and Horovod for advancing large-scale model training.
NVIDIA designed GeForce 256, a chip company widely marketed as the ‘world’s first GPU’, in 1999. The single-chip processor with an integrated transform, lighting, and rendering engine could process a
ClassSR efficiently utilizes the available computational resources to decompose original image, super-resolve and restore it in SR networks.
“If the last 20 years were amazing, the next 20 will seem nothing short of science fiction.” Jensen Huang, CEO, NVIDIA NVIDIA’s CEO, Jensen Huang, kicked off the GPU Technology
In this article, we will employ the AlexNet model provided by the PyTorch as a transfer learning framework with pre-trained ImageNet weights. The network will be trained on the CIFAR-10
CUDA is a parallel computing architecture created by NVIDIA and is specifically designed to be used with NVIDIA GPUs. It is utilised in different sectors of science and research applications
GPU computing has become one of the most important elements of AI infrastructure today. Owing to inherent architectural advantages, GPUs are well-suited to accelerate Deep Learning tasks. NVIDIA provides a
MATLAB provides the ideal environment for deep learning, through to model training and deployment. In this article, we see how MATLAB is gaining in popularity for deep learning
The new technological era is one where task-specific hardware and software are on the rise. This year at Google I/O 2018, Google launched a new generation of Tensor Processing
With the rise in analysis platforms across every sector, leading big tech companies and startups are integrating Machine Learning and Deep Learning in their existing system. This advancement has led
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