AI users and developers can now measure the amount of electricity various AI models consume to complete tasks with an ...

Stephen Whitelam, a researcher whose work spans thermodynamic theory and machine learning, has described a framework for ...

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.

A Queen’s research team has developed a new way to train AI systems so they focus on the bigger picture instead of specific, ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...

The semiconductor industry has been applying AI to accelerate chip design for over three years, achieving 10X productivity gains and focusing primarily on tasks that now seem relatively easy, such as ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

What is a neural network? A neural network, also known as an artificial neural network, is a type of machine learning that works similarly to how the human brain processes information. Instead of ...

Comparison of neuron models in deep neural networks (DNNs) and spiking neural networks (SNNs). (a) Conventional DNNs neuron model processes continuous-valued inputs, where x represents input ...