. 24/7 Space News .
CHIP TECH
Memristor chips that see patterns over pixels
by Staff Writers
Ann Arbor MI (SPX) May 28, 2017


(file illustration only)

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.

Memristors are electrical resistors with memory - advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.

"The tasks we ask of today's computers have grown in complexity," Lu said. "In this 'big data' era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry."

But like neural networks in a biological brain, networks of memristors can perform many operations at the same time, without having to move data around. As a result, they could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning. Memristors are good candidates for deep neural networks, a branch of machine learning, which trains computers to execute processes without being explicitly programmed to do so.

"We need our next-generation electronics to be able to quickly process complex data in a dynamic environment. You can't just write a program to do that. Sometimes you don't even have a pre-defined task," Lu said. "To make our systems smarter, we need to find ways for them to process a lot of data more efficiently. Our approach to accomplish that is inspired by neuroscience."

A mammal's brain is able to generate sweeping, split-second impressions of what the eyes take in. One reason is because they can quickly recognize different arrangements of shapes. Humans do this using only a limited number of neurons that become active, Lu says. Both neuroscientists and computer scientists call the process "sparse coding."

"When we take a look at a chair we will recognize it because its characteristics correspond to our stored mental picture of a chair," Lu said. "Although not all chairs are the same and some may differ from a mental prototype that serves as a standard, each chair retains some of the key characteristics necessary for easy recognition. Basically, the object is correctly recognized the moment it is properly classified--when 'stored' in the appropriate category in our heads."

Similarly, Lu's electronic system is designed to detect the patterns very efficiently--and to use as few features as possible to describe the original input.

In our brains, different neurons recognize different patterns, Lu says.

"When we see an image, the neurons that recognize it will become more active," he said. "The neurons will also compete with each other to naturally create an efficient representation. We're implementing this approach in our electronic system."

The researchers trained their system to learn a "dictionary" of images. Trained on a set of grayscale image patterns, their memristor network was able to reconstruct images of famous paintings and photos and other test patterns.

If their system can be scaled up, they expect to be able to process and analyze video in real time in a compact system that can be directly integrated with sensors or cameras.

The project is titled "Sparse Adaptive Local Learning for Sensing and Analytics." Other collaborators are Zhengya Zhang and Michael Flynn of the U-M Department of Electrical Engineering and Computer Science, Garrett Kenyon of the Los Alamos National Lab and Christof Teuscher of Portland State University.

CHIP TECH
Using graphene to create quantum bits
Lausanne, Switzerland (SPX) May 30, 2017
In the race to produce a quantum computer, a number of projects are seeking a way to create quantum bits - or qubits - that are stable, meaning they are not much affected by changes in their environment. This normally needs highly nonlinear non-dissipative elements capable of functioning at very low temperatures. In pursuit of this goal, researchers at EPFL's Laboratory of Photonics and Qu ... read more

Related Links
University of Michigan
Computer Chip Architecture, Technology and Manufacture
Nano Technology News From SpaceMart.com


Thanks for being here;
We need your help. The SpaceDaily news network continues to grow but revenues have never been harder to maintain.

With the rise of Ad Blockers, and Facebook - our traditional revenue sources via quality network advertising continues to decline. And unlike so many other news sites, we don't have a paywall - with those annoying usernames and passwords.

Our news coverage takes time and effort to publish 365 days a year.

If you find our news sites informative and useful then please consider becoming a regular supporter or for now make a one off contribution.
SpaceDaily Contributor
$5 Billed Once


credit card or paypal
SpaceDaily Monthly Supporter
$5 Billed Monthly


paypal only


Comment using your Disqus, Facebook, Google or Twitter login.

Share this article via these popular social media networks
del.icio.usdel.icio.us DiggDigg RedditReddit GoogleGoogle

CHIP TECH
Conch shells may inspire better helmets, body armor

MIT researchers engineer shape-shifting food

DARPA Picks Design for Next-Generation Spaceplane

SDL-Supported SmallSat Launched from International Space Station

CHIP TECH
Successful launch puts New Zealand in space race

Russia to create new Super-Heavy Class rocket after 2025

Neptune: Neutralizer-free plasma propulsion

Spaceflight buys Electron Rocket from Rocket Lab

CHIP TECH
Preparations Continue Before Driving into 'Perseverance Valley'

Schiaparelli landing investigation completed

HI-SEAS Mission V Mars simulation marks midway point

Deciphering the fluid floorplan of a planet

CHIP TECH
California Woman Charged for Trying to Hand Over Sensitive Space Tech to China

A cabin on the moon? China hones the lunar lifestyle

China tests 'Lunar Palace' as it eyes moon mission

China to conduct several manned space flights around 2020

CHIP TECH
New Target Date for Second Iridium NEXT Launch

Satellite industry supports FCC proposal to reduce internet regulations for service providers

AsiaSat 9 ready for shipment

SES Networks offers new hybrid resiliency service

CHIP TECH
New method allows real-time monitoring of irradiated materials

Neutron lifetime measurements take new shape for in situ detection

Solving the riddle of the snow globe

Bamboo inspires optimal design for lightness and toughness

CHIP TECH
Water forms superstructure around DNA, new study shows

How RNA formed at the origins of life

NASA Scientist Parlays Experience to Build Ocean Worlds Instrument

Scientists propose synestia, a new type of planetary object

CHIP TECH
A whole new Jupiter with first science results from Juno

First results from Juno show cyclones and massive magnetism

Jupiters complex transient auroras

NASA's Juno probe forces 'rethink' on Jupiter









The content herein, unless otherwise known to be public domain, are Copyright 1995-2024 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. General Data Protection Regulation (GDPR) Statement Our advertisers use various cookies and the like to deliver the best ad banner available at one time. All network advertising suppliers have GDPR policies (Legitimate Interest) that conform with EU regulations for data collection. By using our websites you consent to cookie based advertising. If you do not agree with this then you must stop using the websites from May 25, 2018. Privacy Statement. Additional information can be found here at About Us.