A deep belief neural network based on silicon memristive synapses

While artificial intelligence (AI) models are becoming increasingly advanced, training and running these models on conventional computer hardware is very energy consuming. Engineers worldwide have thus been trying to create alternative, brain-inspired hardware that could better support the high computational load of AI systems. Researchers at Technion–Israel Institute of Technology and the Peng Cheng Laboratory have recently created a new neuromorphic computing system supporting deep belief neural networks (DBNs), a generative and graphical class of deep learning models. This system, outlined in Nature Electronics, is based on silicon-based memristors, energy-efficient devices that can both store and process information. Memristors are electrical components that can switch or regulate the flow of electrical current in a circuit, while also remembering the charge that passed through it.