The rapid advancement of artificial intelligence (AI) and machine learning systems has increased the demand for new hardware components that could speed up data analysis while consuming less power. As machine learning algorithms draw inspiration from biological neural networks, some engineers have been working on hardware that also mimics the architecture and functioning of the human brain.
Brain-inspired, or neuromorphic, hardware typically integrates components that mimic the functioning of brain cells, which are thus referred to as artificial neurons. Artificial neurons are connected to one another, with their connections weakening or strengthening over time.
This process resembles synaptic plasticity, the ability of the brain to adapt over time in response to experience and learning. By emulating synaptic plasticity, neuromorphic computing systems could run machine learning algorithms more efficiently, consuming less energy when analyzing large amounts of data and making predictions.
Researchers at Fudan University have recently developed a device based on the ultrathin semiconductor monolayer molybdenum disulfide (MoS₂) that could emulate the adaptability of biological neurons better than other artificial neurons introduced in the past. The new system, introduced in a paper published in Nature Electronics, combines a type of computer memory known as dynamic random-access memory (DRAM) with MoS₂-based circuits.
"Neuromorphic hardware that accurately simulates diverse neuronal behaviors could be of use in the development of edge intelligence," Yin Wang, Saifei Gou and their colleagues wrote in their paper.
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