In reality, the opposite is true: a human brain - which today is still more proficient than CPUs at cognitive tasks like pattern recognition - needs only 20 watts of power to complete a task, while a supercomputer requires more than 50,000 times that amount of energy. For that reason, researchers are turning to neuromorphic computer and artificial neural networks that work more like the human brain. However, with current technology, it is both challenging and expensive to replicate the spatio-temporal processes native to the brain, like short-term and long-term memory, in artificial spiking neural networks (SNN). Feng Xiong, PhD, assistant professor of electrical and computer engineering at the University of Pittsburgh's Swanson School of Engineering, received a $500,000 CAREER Award from the National Science Foundation (NSF) for his work developing the missing element, a dynamic synapse, that will dramatically improve energy efficiency, bandwidth and cognitive capabilities of SNNs. "When the human brain sees rain and then feels wetness, or sees fire and feels heat, the brain's synapses link the two ideas, so in the future, it will associate rain with wetness and fire with warmth. The two ideas are strongly linked in the brain," explains Xiong.
Dec-7-2019, 18:01:53 GMT