Novel Synaptic Architecture for Brain-Inspired Computing

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 the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.