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Research has long focused on developing computers to work as efficiently as our brains. A study, carried out by researchers at the University of Gothenburg, succeeded for the first time in combining a memory function with a computational function in a single component. The discovery paves the way for more efficient technologies, ranging from cell phones to self-driving cars.
In recent years, computers have been able to tackle advanced cognitive tasks, like recognizing language and images or displaying superhuman skills in chess, thanks in large part to artificial intelligence (AI). At the same time, the human brain is still unrivaled in its ability to perform tasks in an efficient and energy efficient manner.
“Finding new ways to perform calculations that resemble energy-efficient brain processes has been a major focus of research for decades. Cognitive tasks, such as image and voice recognition, require significant computing power, and mobile applications, in particular, such as cell phones, drones and satellites, require energy-efficient solutions, â€explains Johan Ã…kerman, professor of spintronics applied at the University of Gothenburg.
Important breakthrough
Together with a research team from Tohoko University, Ã…kerman led a study that has now taken an important step in achieving this goal. In the study, now published in the highly-rated journal Nature Materials, researchers for the first time succeeded in connecting the two main advanced computational tools: oscillator networks and memristors.
Ã…kerman describes oscillators as oscillating circuits that can perform calculations and are comparable to human nerve cells. Memristors are programmable resistors that can also perform calculations and have built-in memory. This makes them comparable to memory cells. The integration of the two is a major advance for researchers.
“This is an important breakthrough because we show that it is possible to combine a memory function with a computational function in the same component. These components function more like the energy efficient neural networks of the brain, their making it possible to become important building blocks in the future, no more brain-like computers. “
Enables energy efficient technologies
According to Johan Ã…kerman, the discovery will enable faster, easier to use and less energy-consuming technologies in many areas. He believes that it is a huge advantage that the research team was able to produce the components in an extremely small footprint: hundreds of components fit into an area equivalent to a single bacterium. This can be especially important in small applications like mobile phones.
“More energy efficient calculations could lead to new features in mobile phones. One example is digital assistants like Siri or Google. Today all processing is done by servers because the calculations require too much energy. for the small size of a phone. If the calculations could instead be done locally, on the actual phone, they could be done faster and easier without needing to connect to the servers. “
He notes that autonomous cars and drones are other examples of where more energy-efficient calculations could lead to developments.
“The more energy-efficient cognitive calculations can be performed, the more possible applications become. This is why our study truly has the potential to advance the field.”
About the research area Neuromorphic computing is an AI-related field that attempts to mimic neural networks in the brain. The research uses new algorithmic approaches that resemble how the human brain integrates with the surrounding world to provide capacity close to human cognition.
Source of the story:
Materials provided by University of Gothenburg. Original written by Ulrika Ernström. Note: Content can be changed for style and length.
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