Engineering

Breaking the scaling limits of analog computing

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up.

Hardware

Superconducting hardware could scale up brain-inspired computing

Scientists have long looked to the brain as an inspiration for designing computing systems. Some researchers have recently gone even further by making computer hardware with a brain-like structure. These "neuromorphic chips" ...

Electronics & Semiconductors

Disposable electronics on a simple sheet of paper

Discarded electronic devices, such as cell phones, are a fast-growing source of waste. One way to mitigate the problem could be to use components that are made with renewable resources and that are easy to dispose of responsibly. ...

Electronics & Semiconductors

A computing in-memory system based on stacked 3D resistive memories

Machine learning architectures based on convolutional neural networks (CNNs) have proved to be highly valuable for a wide range of applications, ranging from computer vision to the analysis of images and the processing or ...

Engineering

Temperature-resistant power semiconductors from a 3D printer

Researchers at the Professorship of Electrical Energy Conversion Systems and Drives at Chemnitz University of Technology have succeeded for the first time in 3D printing housings for power electronic components that are used, ...

Consumer & Gadgets

It's 2022. Why do we still not have waterproof phones?

While manufacturers have successfully increased the water-repelling nature of smartphones, they are still far from "waterproof." A water-resistant product can usually resist water penetration to some extent, but a waterproof ...

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