Go Reconfigure

Programmable logic devices will give us a handheld that does everything—well

10 min read
Illustration: Richard Tuschman
Illustration: Richard Tuschman

Like the idea of a handheld device
that can be any of about 10 different gizmos, depending on your mood? You could soon have one if the ideas described in this two-part report become reality. In this article, Nick Tredennick and Brion Shimamoto of the Gilder Technology Report tell why only programmable logic devices could do the job. Diederik Verkest of the Interuniversity Microelectronics Center in Leuven, Belgium, describes a chameleon-like handheld being developed.

illustrationIllustration: Richard Tuschman

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New Transceiver Receives Power and Data Simultaneously

The beam-steering approach aims to make 5G relays and IoT devices batteryless

3 min read
Side by side images of a gold side of a transceiver with gold squares and the soldering side with an RF integrated circuit.

Prototype of a 64-element millimeter-wave-band phased-array transceiver.

Tokyo Institute of Technology

The quest to transmit electric power wirelessly and over distance has been a goal of electrical engineers since the end of the 19th century, when Nikola Tesla tried his hand at it, to no avail. In the 1970s, NASA and the U.S. Department of Energy engineers achieved some notable successes in wireless power transfer (WPT) in the kilowatt-kilometer range, their efforts spurred on by the energy crises of the time. Interest waned, however, as energy became plentiful again.

Now, with the advent of 5G and its ability to transmit at high frequencies in the millimeter-wave-band range, new opportunities and approaches are opening up for WPT. Researchers at the Tokyo Institute of Technology have developed a prototype 64-element millimeter-wave-band phased-array transceiver that can send and receive data while simultaneously receiving power. The aim is to employ the transceiver initially as a 5G relay, and later to integrate into Internet of Things (IoT) devices. This would enable such devices to shed their batteries, plugs, and cables, says lead researcher Atsushi Shirane. The result would be devices that are smaller, more practical, and capable of speedier communications, with potentially reduced maintenance costs.

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Measuring AI’s Carbon Footprint

New tools track and reduce emissions from machine learning

3 min read
An abstract triangle mosaic background made of circuit board and leaf stock photos
Istock photo

Machine-learning models are growing exponentially larger. At the same time, they require exponentially more energy to train, so that they can accurately process images or text or video. As the AI community grapples with its environmental impact, some conferences now ask paper submitters to include information on CO2 emissions. New research offers a more accurate method for calculating those emissions. It also compares factors that affect them, and tests two methods for reducing them.

Several software packages estimate the carbon emissions of AI workloads. Recently a team at Université Paris-Saclay tested a group of these tools to see if they were reliable. “And they’re not reliable in all contexts,” says Anne-Laure Ligozat, a co-author of that study who was not involved in the new work.

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Improved Dynamic Range for Pulse Detection

Achieving an unprecedented combination of dynamic range and sampling rate for pulse data acquisition

1 min read

Join Teledyne SP Devices for an introduction to our Pulse Detection Range eXtension (PDRX) technology. It achieves a dynamic range equivalent to 16-bit analog-to-digital converters (ADCs) while exceeding the sampling rate supported by commercially available devices. It is ideal for pulse capture in applications such as mass spectrometry. Register now for this free webinar!

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