Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Nvidia’s latest pitch for the future of graphics is not about more polygons or higher memory bandwidth, it is about teaching ...

It’s been ten years since AlexNet, a deep learning convolutional neural network (CNN) model running on GPUs, displaced more traditional vision processing algorithms to win the ImageNet Large Scale ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

Tight PPA constraints are only one reason to make sure an NPU is optimized; workload representation is another consideration.

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

Synopsys has launched a new neural processing unit (NPU) intellectual property (IP) core and toolchain that delivers 3,500 TOPS to support the performance requirements of increasingly complex neural ...

QNAP, a leading provider of network-attached storage (NAS) solutions, has announced the release of the TS-216G, a powerful and versatile 2-bay NAS designed to cater to the needs of individuals, ...