Modeled after the cerebellum, the part of the brain that helps coordinate balance and fine muscle control, the chip is designed to ignore routine information and respond only to unexpected events.
The device could pave the way for highly responsive, low-power AI systems capable of spotting and reacting to unusual events without relying on the massive computing resources of data centers — from always-on health monitors to self-driving cars and autonomous robots.
Rather than processing all incoming information with equal intensity, the brain's biological circuits prioritize important signals and filter out routine background noise, helping it conserve energy.
Neural circuits in the cerebellum contain competing excitatory and inhibitory signals that normally balance each other out. When something unexpected happens, the balance shifts and alerts the brain that it needs to react.
"The cerebellum is excellent at ignoring the expected and reserving its resources for reacting to the unexpected," Hersam said in a statement. "That approach ultimately translates into lower energy consumption."
A map of the human brain, including the cerebellum. (Image credit: grayjay/Shutterstock)Merging memory and compute
One of the constraints is the hardware itself. Processing information involves shuttling data back and forth between separate memory and processing components, resulting in a delay known as the von Neumann bottleneck.
The memtransistor is made from an atomically thin semiconductor called molybdenum disulfide, which forms a channel between two electrodes. One electrode makes direct contact with the semiconductor, while the other sits partly above it, separated by a thin insulating layer.
The device is designed to form the core of the output layer of a larger spiking neural network (SSN), Hersam explained in an email to Live Science.
They then fed the same ECG data into the cerebellum-inspired memtransistor network and a standard transformer model — the AI architecture that underpins large language models (LLMs) — and compared how quickly and efficiently each detected an arrhythmia. The cerebellum-inspired system was more than twice as fast and required around 10,000 times fewer computer calculations, according to the team.
"We have not scaled memtransistors to the level of commercial Si [silicon] chips, but in principle, 2D materials and memtransistors can be scaled to comparable sizes and operating speeds," he told Live Science.
More efficient AI at the edge
A huge potential benefit of the technology is that it could slash AI's reliance on data centers. According to the International Energy Agency, global data center electricity demand could reach around 945 terawatt-hours by 2030 — slightly more than the entire electricity consumption of Japan — largely driven by AI. One terawatt-hour is equal to 1 trillion watt-hours — enough electricity to power a 60-watt lightbulb continuously for 1.9 million years.
Related storiesA hypothetical future memtransistor-based network could enable robots, autonomous vehicles and cybersecurity systems to run continuously at low power by processing data locally and reacting only when they detect an unexpected, potentially hazardous event, the researchers said.
The next stage of research will focus on mimicking the cerebellum's ability to adapt to predictability — specifically, how the human brain stops treating events as unique or unexpected when they're encountered multiple times.
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