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August 18, 2013

Memristor based self organizing adaptive neural network chip could speed image and video processing by 1000 times

Loosely inspired by a biological brain's approach to making sense of visual information, a University of Michigan researcher is leading a project to build alternative computer hardware that could process images and video 1,000 times faster with 10,000 times less power than today's systems—all without sacrificing accuracy.

DARPA has awarded up to $5.7 million to design and fabricate a computer chip based on so-called self-organizing, adaptive neural networks. So far, Lu has received $1.3 million to begin work on the project.

The networks will be made of conventional transistors and innovative components called memristors that perform both logic and memory functions. Memristors are resistors with memory—electronic devices that regulate electric current based on the history of the stimuli applied to them.

Because of their multitasking abilities, researchers say they could enable new computing platforms that can process a vast number of signals in parallel and are capable of advanced machine learning. Systems that utilize them could be much more efficient than conventional computers in handling "big data" tasks such as analyzing images and video.



Lu's ultimate goal in this project is to build a network that uses the memristors as, essentially, artificial synapses between conventional circuits, which could be considered artificial neurons. The synapses in a biological brain are the gaps between neurons across which neurons send chemical or electrical signals.

Lu's ultimate goal in this project is to build a network that uses the memristors as, essentially, artificial synapses between conventional circuits, which could be considered artificial neurons. The synapses in a biological brain are the gaps between neurons across which neurons send chemical or electrical signals.


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