IBM's Neuromorphic Chip

IBM's neuromorphic chip has a one million neuron brain-inspired processor. It is capable of 46 billion synaptic operations per second, per watt.

Deep learning efforts today are run on standard computer hardware using convolutional neural networks. Indeed the approach has proven powerful by pioneers such as Google and Microsoft. In contrast neuromorphic computing, whose spiking neuron architecture more closely mimics human brain function, has generated less enthusiasm in the deep learning community. Now, work by IBM using its TrueNorth chip as a test case may bring deep learning to neuromorphic architectures.

Writing in the Proceedings of the National Academy of Science (PNAS) in August (Convolutional networks for fast, energy-efficient neuromorphic computing), researchers from IBM Research report, “[We] demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, perform inference while preserving the hardware’s underlying energy-efficiency and high throughput.”

Roger Zelazny wrote about immensely capable neuristor brains in his 1976 novella My Name Is Legion:

The full sensitivity of the neuristor brain was not appreciated at first. It was assumed that the operators were adding data in a linear fashion and that this would continue until a critical mass was achieved, corresponding to the construction of a model or picture of the world which would then serve as a point of departure for growth of the Hangman's own mind. And it did seem to check out this way...
(Read more about Zelazny's neuristor brains)

If you want to go further back, take a look at the artificial brain from The Metal Giants, by Edmond Hamilton, published by Weird Tales in 1926.

Via Next Big Future.

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