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 ...

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 ...

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, ...

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 ...

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 ...

Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for ...

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 ...

A new microcontroller claims to offer hardware-assisted machine learning (ML) acceleration for the Internet of Things (IoT) and industrial applications such as smart home, security surveillance, ...

As artificial intelligence becomes increasingly critical to the everyday workflow of enterprises, including increasing usage within security, computer scientists in the AI community are attempting to ...