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IBM, its Research Alliance partners Globalfoundries and Samsung, and equipment suppliers have developed an industry-first process to build silicon nanosheet transistors that will enable 5 nanometer (nm) chips. The details of the process will be presented at the 2017 Symposia on VLSI Technology and Circuits conference in Kyoto, Japan. In less than two years since developing a 7nm test node chip with 20 billion transistors, scientists have paved the way for 30 billion switches on a fingernail-sized chip.

The resulting increase in performance will help accelerate cognitive computing, the Internet of Things (IoT), and other data-intensive applications delivered in the cloud. The power savings could also mean that the batteries in smartphones and other mobile products could last two to three times longer than today’s devices, before needing to be charged.

Scientists working as part of the IBM-led Research Alliance at the SUNY Polytechnic Institute Colleges of Nanoscale Science and Engineering’s NanoTech Complex in Albany, NY achieved the breakthrough by using stacks of silicon nanosheets as the device structure of the transistor, instead of the standard FinFET architecture, which is the blueprint for the semiconductor industry up through 7nm node technology.

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Rice University computer scientists have adapted a widely used technique for rapid data lookup to slash the amount of computation — and thus energy and time — required for deep learning, a computationally intense form of machine learning.

“This applies to any deep-learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be,” said lead researcher Anshumali Shrivastava, an assistant professor of computer science at Rice.

The research will be presented in August at the KDD 2017 conference in Halifax, Nova Scotia. It addresses one of the biggest issues facing tech giants like Google, Facebook and Microsoft as they race to build, train and deploy massive deep-learning networks for a growing body of products as diverse as self-driving cars, language translators and intelligent replies to emails.

A study by neuroscientists at Toronto-based Baycrest Rotman Research Institute and Stanford University involving playing a musical instrument suggests ways to improve brain rehabilitation methods.

In the study, published in the Journal of Neuroscience on May 24, 2017, the researchers asked young adults to listen to sounds from an unfamiliar musical instrument (a Tibetan singing bowl). Half of the subjects (the experimental group) were then asked to recreate the same sounds and rhythm by striking the bowl; the other half (the control group) were instead asked to recreate the sound by simply pressing a key on a computer keypad.

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