Semiconductor Engineering

Convolutional Neural Networks: Co-Design of Hardware Architecture and Compression Algorithm

“Over the past decade, deep-learning-based representations have demonstrated remarkable performance in academia and industry. The learning capability of convolutional neural networks (CNNs) originates ...
TMCnet

WiMi Lays Out Scalable Quantum Convolutional Neural Network to Enhance Image Classification Accuracy and Efficiency

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that they are actively exploring Scalable ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
pv magazine International

PV energy forecasting based on genetic algorithms, dynamic neural network

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

Researchers Propose a Novel Representation Learning Framework to Address the Lack of Causal Characterization in Deep Learning Models

Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...
Electronic Design

A Neural-Network-Based Approach to Smarter DPD Engines

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...
Quanta Magazine

Will Transformers Take Over Artificial Intelligence?

Imagine going to your local hardware store and seeing a new kind of hammer on the shelf. You’ve heard about this hammer: It pounds faster and more accurately than others, and in the last few years ...