OnScale: Driving 5G Innovation

We all want 5G smartphones that can live stream high-definition selfies, 5G augmented reality gear or 5G drones that can broadcast 4K video from anywhere on the planet.

1 min read
OnScale

We all want 5G smartphones that can live stream high-definition selfies, 5G augmented reality gear or 5G drones that can broadcast 4K video from anywhere on the planet. But analysts say 5G devices aren’t coming any time soon. Let’s take a deeper look at why.

For 5G mobile device like smartphones, the challenge engineers face is miniaturizing and optimizing the performance of the radio frequency front end. The RF front-end module consists of filters, amplifiers and switches to manage gigahertz RF signals.

Figure 1

Filters for 5G bands are especially challenging to optimize, and in a 5G smartphone there will be dozens of these tiny filters. In fact, the only way to truly optimize RF filters is by using computer-aided engineering simulation and optimization. And the only CAE platform capable of optimizing next-generation RF filters is OnScale.

Figure 2 Full 3D TC-SAW simulation – 50M DoF solved in under 12 hours

OnScale is an extremely powerful, on-demand, scalable cloud CAE platform that breaks performance barriers for engineers optimizing next-generation 5G RF filters.

With OnScale Cloud, engineers can analyze hundreds, thousands, even millions of design concepts rapidly in parallel on thousands of HPCs in the cloud. With this amount of computational power, engineers can explore massive design spaces and find optimal designs quickly, all while slashing R&D cost, risk and time to market.

To learn more about how OnScale is driving 5G Innovation, please visit www.OnScale.com/5G.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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