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 ...

PPA constraints need to be paired with real workloads, but they also need to be flexible to account for future changes.

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

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 ...

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

Parth is a technology analyst and writer specializing in the comprehensive review and feature exploration of the Android ecosystem. His work is distinguished by its meticulous focus on flagship ...

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, ...

Emergence of new applications and use cases: Neural networks are being applied to an increasingly diverse range of applications, including computer vision, natural language processing, fraud detection ...

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 ...

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 ...