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 model is based on silicon based memristors, which are energy-efficient devices that can both store and process information. The artificial synapses were fabricated using commercial complementary-metal-oxide-semiconductor (CMOS) processes. These silicon based synapses have numerous advantageous features, including analogue tunability, high endurance, long retention time, predictable cycling degradation, and moderate variability across different devices. The results showed over 97% accurate recognition of handwritten digits when using Y-Flash based memristors. Memristors measured in a probe station (Source: https://techxplore.com)
AI system learns concepts shared across video, audio, and text
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