Lead author Yiyank Li, University of Michigan
A new method to make non-volatile computer memory may have unlocked a problem been holding back machine learning and has the potential to revolutionize technologies like voice recognition, image processing and autonomous driving, according to a team of researchers at Sandia National Laboratories.
Working with collaborators from the University of Michigan, the Sandia team published a paper in the peer-reviewed journal Advanced Materials discussing a new method to imbue computer chips that power machine-learning applications with more processing power by using a common material found in house paint in an analog memory device that enables highly energy-efficient machine inference operations.
“Titanium oxide is one of the most commonly made materials,” said Sandia materials scientist Alec Talin. “Every paint you buy has titanium oxide in it. It’s cheap and nontoxic. It’s an oxide, there’s already oxygen there. But if you take a few out, you create what are called oxygen vacancies. It turns out that when you create oxygen vacancies, you make this material electrically conductive.”
Those oxygen vacancies can now store electrical data, giving almost any device more computing power, according to the researchers. The researchers create the oxygen vacancies by heating a computer chip with a titanium oxide coating above 302 degrees fahrenheit (150 degree celsius), separate some of the oxygen molecules from the material using electrochemistry and create vacancies.
“When it cools off, it stores any information you program it with,” Talin said.
Right now, computers generally work by storing data in one place and processing it in another, requiring constant data transfer from one place to the next, consuming energy and compute resources. Lead author of the Advanced Materials paper, Yiyang Li, a former Truman Fellow at Sandia and now an assistant professor of materials science at Michigan, explained how the new process could fundamentally change how computers work.
Alec Talin, Sandia
“What we’ve done is make the processing and the storage at the same place,” Li said. “What’s new is that we’ve been able to do it in a predictable and repeatable manner.”
Li and Talin view oxygen vacancies as a way to help machine learning overcome a big obstacle holding it back — power consumption.
“If we are trying to do machine learning, that takes a lot of energy because you are moving it (data) back and forth and one of the barriers to realizing machine learning is power consumption,” Li said. “If you have autonomous vehicles, making decisions about driving consumes a large amount of energy to process all the inputs. If we can create an alternative material for computer chips, they will be able to process information more efficiently, saving energy and processing a lot more data.”
Talin said the potential of oxygen vacancies is far-reading.
“Think about your cell phone,” he said. “If you want to give it a voice command, you need to be connected to a network that transfers the command to a central hub of computers that listen to your voice and then send a signal back telling your phone what to do. Through this process, voice recognition and other functions happen right in your phone.”
Talin said the team is working on refining several processes and testing the method on a larger scale. The project is funded through Sandia’s Laboratory Directed Research and Development program.
source: Sandia
