New AI Transistor Works Just Like the Human Brain

Longtime Slashdot reader FudRucker quotes a report from Study Finds: Researchers from Northwestern University, Boston College, and the Massachusetts Institute of Technology (MIT) have developed a new synaptic transistor that works just like the human brain. This advanced device, capable of both processing and storing information simultaneously, marks a notable shift from traditional machine-learning tasks to performing associative learning -- similar to higher-level human cognition. This study introduces a device that operates effectively at room temperatures, a notable improvement over previous brain-like computing devices that required extremely cold conditions to keep their circuits from overheating. With its fast operation, low energy consumption, and ability to retain information without power, the new transistor is well-suited for real-world applications. "The brain has a fundamentally different architecture than a digital computer," says study co-author Mark Hersam, the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern's McCormick School of Engineering, in a university release. "In a digital computer, data move back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain." Hersam and his team employed a novel strategy involving moire patterns, a type of geometric design formed when two patterns are overlaid. By stacking two-dimensional materials like bilayer graphene and hexagonal boron nitride and twisting them to form a moire pattern, they could manipulate the electronic properties of the graphene layers. This manipulation allowed for the creation of a synaptic transistor with enhanced neuromorphic functionality at room temperature. The device's testing involved training it to recognize patterns and similarities, a form of associative learning. For instance, if trained to identify a pattern like "000," the transistor could distinguish that "111" is more similar to "000" than "101," demonstrating a higher level of cognitive function. This ability to process complex and imperfect inputs has significant implications for real-world AI applications, such as improving the reliability of self-driving vehicles in challenging conditions. The study has been published in the journal Nature.

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