Page 13: Research news on Neuromorphic AI hardware

Neuromorphic AI hardware encompasses brain-inspired computing systems that implement neural network primitives directly in physical substrates to achieve extreme energy efficiency and low latency. Architectures use devices such as memristors, magnetic tunnel junctions, electrochemical memories, photonic and microwave components, and organic or superconducting neurons to realize synapses, neurons, and compute-in-memory operations. These platforms support spiking and analog neural computation, on-chip learning, and specialized sensory and cognitive functions, targeting applications from edge intelligence and autonomous systems to large-scale AI acceleration and brain–computer interfaces.

Electronics & Semiconductors

Light-based chip can boost power efficiency of AI tasks up to 100-fold

A team at the University of Florida has developed a new kind of computer chip that uses light with electricity to perform one of the most power-intensive parts of artificial intelligence—image recognition and similar pattern-finding ...

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