To Speed Up AI, Mix Memory and Processing

New computing architectures aim to extend artificial intelligence from the cloud to smartphones

3 min read
Image: Sujan Gonugondla
Image: Sujan Gonugondla

/image/MzAzMzY2Mw.jpeg Tearing Down Walls: This prototype features a new chip design called deep in-⁠memory architecture. Image: Sujan Gonugondla

If John von Neumann were designing a computer today, there’s no way he would build a thick wall between processing and memory. At least, that’s what computer engineer Naresh Shanbhag of the ­University of Illinois at Urbana-Champaign believes. The eponymous von Neumann architecture was published in 1945. It enabled the first stored-memory, reprogrammable computers—and it’s been the backbone of the industry ever since.

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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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