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Could Supercomputing Turn to Signal Processors (Again)?

Texas team says digital signal processors could compete in high-performance computing

3 min read
Illustration: Gluekit
Image: Gluekit

Illustration: Gluekit Illustration: Gluekit

Building high-performance computers used to be all about maximizing flops, or floating-point operations per second. But the engineers designing today’s high-performance systems are keeping a close eye not just on the number of flops but also on flops per watt. Judged by that energy-efficiency metric, some digital-signal processing (DSP) chips—the sophisticated signal conditioners that run our wireless networks, among other things—might make promising building blocks for future supercomputers, recent research suggests/

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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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