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Memristors power quick-learning neural network

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The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.


Memristor Computing On A Chip

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However, researchers at the University of Michigan are claiming the first memristor-based programmable computer that has the potential to make AI applications more efficient and faster. Because memristors have a memory, they can accumulate data in a way that is common for -- among other things -- neural networks. The chip has both an array of nearly 6,000 memristors, a crossbar array, along with analog to digital and digital to analog converters. In fact, there are 486 DACs and 162 ADCs along with an OpenRISC processor. According to the paper, the chip turned in 188 billion operations per second per watt while consuming about 300 mW of power.



Next-gen computing: Memristor chips that see patterns over pixels

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Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology. Lu's next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called "sparse coding" to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos. Memristors are electrical resistors with memory -- advanced electronic devices that regulate current based on the history of the voltages applied to them.


Next-gen computing: Memristor chips that see patterns over pixels

#artificialintelligence

Inspired by how mammals see, a new "memristor" computer circuit prototype at the University of Michigan has the potential to process complex data, such as images and video orders of magnitude, faster and with much less power than today's most advanced systems. Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology. Lu's next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called "sparse coding" to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.