SpiNNaker: ‘Human brain’ computer switched on TODAY



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SpiNNaker

BEAST: The computer took more than 10 years to build (Pic: UNIVERSITY OF MANCHESTER / GETTY)

The £15million machine, dubbed “SpiNNaker”, can complete 200 million million actions per second.

Construction of the supercomputer began in 2006 — and each of its chips contain 100 million moving parts.

The monster-sized machine was designed and built in The University of Manchester’s School of Computer Science.

Steve Furber, Professor of Computer Engineering, came up with the idea of a “human brain” thinking machine.

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Human brain

NEXT LEVEL: Could this computer brain teach us more about ourselves? (Pic: GETTY)

“We’ve essentially created a machine that works more like a brain than a traditional computer”

Prof Steve Furber

He said: “SpiNNaker completely re-thinks the way conventional computers work.

“We’ve essentially created a machine that works more like a brain than a traditional computer, which is extremely exciting.

“The ultimate objective for the project has always been a million cores in a single computer for real time brain modelling applications, and we have now achieved it, which is fantastic.”

Instead of sending huge amounts of information from A to B, the computer sends small amounts to different areas simultaneously — like a brain.

SpiNNaker

GENIUS: SpiNNaker can complete 200 million million actions per second (Pic: UNIVERSITY OF MANCHESTER)

SpiNNaker will be used give insight into how the human brain works.

It has simulated a region of the brain involved in Parkinson’s disease, sparking hope it could help scientists make breakthroughs.

SpiNNaker is so powerful its system is being used to develop artificial intelligence robots that can “see” like humans and move without bumping into things.

Prof Furber added: “Neuroscientists can now use SpiNNaker to help unlock some of the secrets of how the human brain works by running unprecedentedly large scale simulations.

“It also works as real-time neural simulator that allows roboticists to design large scale neural networks into mobile robots so they can walk, talk and move with flexibility and low power.”

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