Energy and memory: A new neural network paradigm A dynamic energy landscape is at the heart of theorists' new model of memory retrieval Date: May 14, 2025 Source: University of California - Santa ...

These IEDM papers and other papers from the MRAM Global Innovation Forum provided insights on the future of non-volatile storage and their uses in future computing applications, including various ...

Abstract: “In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning ...

Training a neural network in phase-change memory beats GPUs Specialized hardware that trains in-memory is both fast and energy-efficient.

Scientist, for the first time, have developed a computer algorithm that is nearly as accurate as people are at mapping brain neural networks -- a breakthrough that could speed up the image ...

Titans architecture complements attention layers with neural memory modules that select bits of information worth saving in the long term.

Even when neural network calculations are clearly identifiable as a limiting factor, the exact algorithms involved differ. Neural network research is advancing faster than integrated circuit design ...

In 1982 physicist John Hopfield translated this theoretical neuroscience concept into the artificial intelligence realm, with the formulation of the Hopfield network. In doing so, not only did he ...