Page 8: 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.

Machine learning & AI

Small changes make some AI systems more brain-like than others

Artificial intelligence systems that are designed with a biologically inspired architecture can simulate human brain activity before ever being trained on any data, according to new research from Johns Hopkins University.

Electronics & Semiconductors

Tech firms from Dell to HP warn of memory chip squeeze from AI

Dell Technologies Inc., HP Inc. and other tech companies are warning of potential memory-chip supply shortages in the coming year due to soaring demand from the build-out of artificial intelligence infrastructure.

Computer Sciences

Visualizing the internal structure behind AI decision-making

Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze ...

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