Page 7: 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

Jumping spiders inspire ultra-efficient 3D camera

By borrowing a trick from tiny jumping spiders, Northwestern University engineers have developed an extremely energy-efficient 3D camera. Called SpiderCam, the new device senses depth the same way that jumping spiders judge ...

Consumer & Gadgets

Robots, supply strain: Five hot topics at Computex

From laptops designed for the artificial intelligence era to advances in robotics and sky-high tech shares, here are five hot topics at Taipei's huge Computex trade show:

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