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2D-Materials-Based Electronic Circuits (KAUST and TSMC)


A special edition article titled "Electronic Circuits made of 2D Materials" was just published by Dr. Mario Lanza, KAUST Associate Professor of Material Science and Engineering, and Iuliana Radu, corporate researcher at TSMC. This special issue covers 21 articles from leading subject matter experts, ranging from materials synthesis and their integration in micro/nano-electronic devices and c... » read more

3 Emerging Technologies: Memristors, Spintronics & 2D Materials


New technical paper titled "Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware" from researchers at University College London and University of Cambridge. Abstract "In a data-driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological pr... » read more

AI-Powered Verification


With functional verification consuming more time and effort than design, the chip industry is looking at every possible way to make the verification process more effective and more efficient. Artificial intelligence (AI) and machine learning (ML) are being tested to see how big an impact they can have. While there is progress, it still appears to be just touching the periphery of the problem... » read more

Easier And Faster Ways To Train AI


Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

There’s More To Machine Learning Than CNNs


Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Making Sense Of New Edge-Inference Architectures


New edge-inference machine-learning architectures have been arriving at an astounding rate over the last year. Making sense of them all is a challenge. To begin with, not all ML architectures are alike. One of the complicating factors in understanding the different machine-learning architectures is the nomenclature used to describe them. You’ll see terms like “sea-of-MACs,” “systolic... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

System Bits: Sept. 24


Quantum states Many companies and academic researchers are working on quantum computing technology, including the University of Buffalo. New research on two-dimensional tungsten disulfide (WS2) could open the door to advances in quantum computing, UB reports. In a paper published Sept. 13 in Nature Communications, scientists report that they can manipulate the electronic properties of th... » read more

Optimizing Deep-Learning Inference For Embedded Devices


Deep artificial neural networks (ANNs) have emerged as universal feature extractors in various tasks as they approach (and in many cases surpass) human-level performance. They have become fundamental building blocks of almost every modern artificially intelligent (AI) application, from online shop recommendations to self-driving cars. This whitepaper highlights how different challenges relat... » read more

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