New discovery opens the best way for brain-like computer systems — ScienceDaily


Analysis has lengthy strived to develop computer systems to work as vitality effectively as our brains. A examine, led by researchers on the College of Gothenburg, has succeeded for the primary time in combining a reminiscence perform with a calculation perform in the identical element. The invention opens the best way for extra environment friendly applied sciences, all the things from cellphones to self-driving vehicles.

In recent times, computer systems have been capable of sort out superior cognitive duties, like language and picture recognition or displaying superhuman chess abilities, thanks largely to synthetic intelligence (AI). On the identical time, the human mind continues to be unmatched in its capability to carry out duties successfully and vitality effectively.

“Discovering new methods of performing calculations that resemble the mind’s energy-efficient processes has been a serious objective of analysis for many years. Cognitive duties, like picture and voice recognition, require vital laptop energy, and cell purposes, particularly, like cellphones, drones and satellites, require vitality environment friendly options,” says Johan Åkerman, professor of utilized spintronics on the College of Gothenburg.

Necessary breakthrough

Working with a analysis staff at Tohoko College, Åkerman led a examine that has now taken an vital step ahead in reaching this objective. Within the examine, now printed within the extremely ranked journal Nature Supplies, the researchers succeeded for the primary time in linking the 2 essential instruments for superior calculations: oscillator networks and memristors.

Åkerman describes oscillators as oscillating circuits that may carry out calculations and which can be akin to human nerve cells. Memristors are programable resistors that may additionally carry out calculations and which have built-in reminiscence. This makes them akin to reminiscence cells. Integrating the 2 is a serious development by the researchers.

“This is a vital breakthrough as a result of we present that it’s potential to mix a reminiscence perform with a calculating perform in the identical element. These elements work extra just like the mind’s energy-efficient neural networks, permitting them to grow to be vital constructing blocks in future, extra brain-like computer systems.”

Allows energy-efficient applied sciences

In response to Johan Åkerman, the invention will allow quicker, simpler to make use of and fewer vitality consuming applied sciences in lots of areas. He feels that it’s a enormous benefit that the analysis staff has efficiently produced the elements in an especially small footprint: a whole lot of elements match into an space equal to a single bacterium. This may be of specific significance in smaller purposes like cellphones.

“Extra energy-efficient calculations may result in new performance in cellphones. An instance is digital assistants like Siri or Google. In the present day, all processing is finished by servers because the calculations require an excessive amount of vitality for the small measurement of a cellphone. If the calculations may as a substitute be carried out domestically, on the precise cellphone, they may very well be performed quicker and simpler with out a want to connect with servers.”

He notes self-driving vehicles and drones as different examples of the place extra energy-efficient calculations may drive developments.

“The extra energy-efficiently that cognitive calculations could be carried out, the extra purposes grow to be potential. That is why our examine actually has the potential to advance the sector.”

Concerning the analysis area Neuromorphic computing is an AI-related area making an attempt to mimic the mind’s neural networks. The analysis makes use of new algorithmic approaches that resemble how the human mind integrates with the encircling world to ship capability approaching human cognition.

Story Supply:

Supplies supplied by College of Gothenburg. Authentic written by Ulrika Ernström. Observe: Content material could also be edited for model and size.


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