Page 19: 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.

Engineering

Researchers unveil nearly invisible brain-computer interface

Georgia Tech researchers have developed an almost imperceptible microstructure brain sensor to be inserted into the minuscule spaces between hair follicles and slightly under the skin. The sensor offers high-fidelity signals ...

Hi Tech & Innovation

Self-organizing 'infomorphic neurons' can learn independently

Researchers have developed "infomorphic neurons" that learn independently, mimicking their biological counterparts more accurately than previous artificial neurons. A team of researchers from the Göttingen Campus Institute ...

Computer Sciences

Humans as hardware: Computing with biological tissue

Most computers run on microchips, but what if we've been overlooking a simpler, more elegant computational tool all this time? In fact, what if we were the computational tool?

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