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Everything you need to know about neuromorphic computing


In July, a group of artificial intelligence researchers showcased a self-driving bicycle that could navigate around obstacles, follow a person, and respond to voice commands. While the self-driving bike itself was of little use, the AI technology behind it was remarkable. Powering the bicycle was a neuromorphic chip, a special kind of AI computer.

Neuromorphic computing is not new. In fact, it was first proposed in the 1980s. But recent developments in the artificial intelligence industry have renewed interest in neuromorphic computers.

The growing popularity of deep learning and neural networks has spurred a race to develop AI hardware specialized for neural network computations. Among the handful of trends that have emerged in the past few years is neuromorphic computing, which has shown promise because of its similarities to biological and artificial neural networks.

Read: [How machines see: everything you need to know about computer vision]

How deep neural networks work

At the heart of recent advances in artificial intelligence are artificial neural networks (ANN), AI software that roughly follows the structure of the human brain. Neural networks are composed of artificial neurons, tiny computation units that perform simple mathematical functions.

Artificial neurons aren’t of much use alone. But when you stack them up in layers, they can perform remarkable tasks, such as detecting objects in images and transforming voice audio to text. Deep neural networks can contain hundreds of millions of neurons, spread across dozens of layers.