Novel synaptic architecture for brain inspired computing

The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. They were published last week in a paper in the journal Nature Communications. The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications. Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks -- mathematical models of the neurons and synapses of the brain -- that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.