Energy-efficient neural network computing represents a transformative approach to mitigating the increasing energy demands of modern artificial intelligence systems. By harnessing cutting-edge ...

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

Artificial intelligence grows more demanding every year. Modern models learn and operate by pushing huge volumes of data through repeated matrix operations that sit at the heart of every neural ...

A standard digital camera used in a car for stuff like emergency braking has a perceptual latency of a hair above 20 milliseconds. That’s just the time needed for a camera to transform the photons ...

As part of this week’s SPIE Optics & Photonics conference program Emerging Topics in Artificial Intelligence, Asst. Prof. Logan G. Wright, of Yale University, presented an invited paper, entitled ...

The deep neural network models that power today’s most demanding machine-learning applications are pushing the limits of traditional electronic computing hardware, according to scientists working on a ...

A research team from Peking University has successfully developed a vanadium oxide (VO₂)-based “locally active memristive ...

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 ...