Page 16: 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.

Software

No-code machine learning development tools

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited AI expertise in industrial fields such as factories, medical, and shipbuilding, providing them with ...

Energy & Green Tech

New analog computing method slashes AI training energy use

Artificial intelligence is getting more powerful—but it's also racking up a massive energy bill. Some estimate that one maximum-length ChatGPT query can use about twice as much power as an average U.S. home does in one minute. ...

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