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ELEKTRO 4-5/2020 was released on May 6th 2020. Its digital version will be available immediately.

Topic: Electroinstallation; Lightning and overvoltage protection

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Energy law novel: An end to energy scammers

SVĚTLO (Light) 2/2020 was released on March 6th 2020. Its digital version will be available immediately.

Market, business, enterprise
BOOBA in new showroom, which surpassed all expectations
Discourse with Technology of Capital city Prague chairman of management

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Modern methods of gaining dates for processing lighting technology assessment

Three-Dimensional Neural Networks

4. 5. 2020 | University of Massachusetts | www.massachusetts.edu

Researchers at the University of Massachusetts and the Air Force Research Laboratory Information Directorate have recently created a 3-D computing circuit that could be used to map and implement complex machine learning algorithms, such convolutional neural networks (CNNs). This 3-D circuit, presented in a paper published in Nature Electronics, comprises eight layers of memristors; electrical components that regulate the electrical current flowing in a circuit and directly implement neural network weights in hardware.

"Previously, we developed a very reliable memristive device that meets most requirements of in-memory computing for artificial neural networks, integrated the devices into large 2-D arrays and demonstrated a wide variety of machine intelligence applications," Prof. Qiangfei Xia, one of the researchers who carried out the study, told TechXplore. "In our recent study, we decided to extend it to the third dimension, exploring the benefit of a rich connectivity in a 3-D neural network."

3D memristors

"The fully connected topology of almost all existing memristor devices does not match the complex topologies of modern neural networks, such as CNNs, the most prominent computational techniques currently used for computer vision applications," Lin explained. "As a result, efficient implementation of a convolutional neural network in a memristor system becomes extremely challenging."

Read more at University of Massachusetts

Image Credit: University of Massachusetts

-jk-