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 ...

This does not happen often. Deep learning is the hottest technology today, with countless applications and deep investment from the usual suspects. To have something new released from someone who is ...

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 ...

At the MIT EmTech Digital conference, startup Nervana announced plans to design and build a custom ASIC processor for neural networks and machine learning applications that the company’s CEO, Naveen ...

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 ...

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 ...

Developers and systems designers have a number of options available to them for adding some form of neural-networking or deep-learning capability to their embedded designs. Early on — and even today — ...

Recall a phone number or directions just recited and your brain will be actively communicating across many regions. It is thought that working memory relies on interactions between these regions, but ...