Researchers at the University of Pittsburgh’s Swanson School of Engineering have developed an artificial synapse that processes information by mimicking the analog way that the human brain completes tasks.
“The analog nature and massive parallelism of the brain are partly why humans can outperform even the most powerful computers when it comes to higher order cognitive functions such as voice recognition or pattern recognition in complex and varied data sets,” explains Dr. Feng Xiong, assistant professor of electrical and computer engineering.
The emerging field of neuromorphic computing focuses on the design of computational hardware that is inspired by the human brain. The team at Pittsburgh built graphene-based artificial synapses in a two-dimensional honeycomb configuration of carbon atoms. Thanks to graphene’s conductive properties, the researchers were able to finely tune its electrical conductance. Just like biological synapses in the brain, the graphene synapse demonstrated excellent energy efficiency.
“It is really important we develop new device concepts for synaptic electronics that are analog in nature, energy-efficient, scalable, and suitable for large-scale integrations,” explains Xiong. “Our graphene synapse seems to check all the boxes on these requirements so far.”
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