A Survey of Neuromorphic Computing and Neural Networks in Hardware

Neuromorphic Computing: How Brain-Like Chips Could Change Everything

Imagine computers that learn more like our brains, not like the old rule-following machines.
That's the idea behind neuromorphic work — building tiny networks that mimic neurons and synapses so machines can adapt and find new solutions by themselves.
Researchers want brain-like behavior from silicon, and they design new ways for devices to sense, react, and keep learning.
The promise is big: low power, fast responses, machines that improve over time — but it's not simple.
To get there teams must understand messy brain wiring, invent new materials, and write fresh software so the hardware can learn.
This article looks back decades, shows where the field grew, and points out what still needs fixing.
Some chips already do simple tasks, other ideas still wait for better parts or smarter code, and many challenges remain.
If these problems are solved the world could see smarter phones, robots that adapt, and new tools for science.
The road is long, but the potential for learning chips and new devices is clear — it may reshape the future.

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A Survey of Neuromorphic Computing and Neural Networks in Hardware

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