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DARPA Joins Public-Private Partnership to Address Microelectronics Advancement

Aspinity s Awesome Analog Artificial Neural Networks (AANNs) – EEJournal

by Max Maxfield I’m a digital logic designer by trade. In an ever-changing and increasingly unreliable world, I find the certainty of Boolean equations to be extremely reassuring. You know where you are with a Karnaugh map, you can trust a De Morgan transformation, and you can gratify your desire for single-bit transitions with a Gray code. By comparison, I find the wibbly-wobbly nature of analog electronics to be somewhat disconcerting, which is unfortunate because there’s so much of it about these days. One of the funny things is that “analog” was supposed to be the stuff of yesteryear. When I was coming up in electronics, digital functions were expensive in terms of transistors, and transistors were expensive in terms of cold, hard cash. That’s one of the reasons why analog computers hung on for decades after their digital counterparts first arrived on the scene. It was possible to implement analog signal processing (ASP) functions using a handful o

The Future of Neuromorphic Computing

The Future of Neuromorphic Computing 5 hours ago Neuromorphic Computing Devices and Materials The information age runs on “data as the new oil”, and the volume of data consumed by the world is expected to grow from around 50 zettabytes (ZB) in 2020 to around 170ZB by 2025. The COVID pandemic led to a big increase in data streams to support telepresence for healthcare, shopping, work from home (WFH), and school from home (SFH). Semiconductor integrated circuits (ICs) are the engines of the information age, built using atomic-scale devices in communications, logic and memory chips. A global market for critical materials worth approximately $50B (US dollars) supports a semiconductor manufacturing industry that produces greater than $400B in annual revenues.

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