(NextGOV) As AI penetrates more of daily life, a heavy reliance on software with massive energy needs is not sustainable. If hardware and software could share intelligence features, an area of silicon might be able to achieve more with a given input of energy. Shriram Ramanathan, a professor of materials engineering at Purdue University, has been able to demonstrate artificial “tree-like” memory in a piece of potential hardware at room temperature. Researchers in the past have only been able to observe this kind of memory in hardware at temperatures that are too low for electronic devices.
The hardware that Ramanathan’s team developed is made of a so-called quantum material. These materials are known for having properties that classical physics cannot explain.
Software uses tree-like memory to organize information into various “branches,” making that information easier to retrieve when learning new skills or tasks.
The way the human brain categorizes information and makes decisions strategy inspired the strategy. “Humans memorize things in a tree structure of categories. We memorize ‘apple’ under the category of ‘fruit’ and ‘elephant’ under the category of ‘animal,’ for example,” says Hai-Tian Zhang, a postdoctoral fellow in the College of Engineering. “Mimicking these features in hardware is potentially interesting for brain-inspired computing.”
“We can build up many thousands of memory states in a quantum material called neodymium nickel oxide by taking advantage of quantum mechanical effects. The material stays the same. We are simply shuffling around protons,” Ramanathan says.
The demonstration of these trees at room temperature in a material is a step toward showing that hardware could offload tasks from software. “This discovery opens up new frontiers for AI that have been largely ignored because implementing this kind of intelligence into electronic hardware didn’t exist,” Ramanathan says.
Research Indicates that Hardware Developed with Quantum Material Such as Neodymium Nickel Oxide Could Cut AI Power Needs
