Unique memristor design with analog switching shows promise for high-efficiency neuromorphic computing
The growing use of artificial intelligence (AI)-based models is placing greater demands on the electronics industry, as many of these models require significant storage space and computational power. Engineers worldwide have thus been trying to develop neuromorphic computing systems that could help meet these demands, many of which are based on memristors.
The human brain has been called the most complicated object in the universe. Trying to replicate that still-unmatched capability for computing, scientists at Los Alamos National Laboratory have made a new interface-type memristive device, which their results suggest can be used to build artificial synapses for next-generation neuromorphic computing.
Researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published today in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which…
Modern artificial intelligence systems rely on moving large amounts of data between memory and processors, a design that limits speed and increases energy use. The human brain works differently: it combines memory and computation within synapses, allowing fast, efficient learning and perception. Replicating this approach in hardware is a central…