Bringing Big Neural Networks to Self-Driving Cars, Smartphones, and Drones

Engineers are trying to squeeze outsize AI into mobile systems

4 min read
Bringing Big Neural Networks to Self-Driving Cars, Smartphones, and Drones
What’s Your Intention? Engineers demonstrated a neural network chip for cars that predicts the risk of hitting a pedestrian.
Photo: KAIST

Artificial intelligence systems based on neural networks have had quite a string of recent successes: One beat human masters at the game of Go, another made up beer reviews, and another made psychedelic art. But taking these supremely complex and power-hungry systems out into the real world and installing them in portable devices is no easy feat. This February, however, at the IEEE International Solid-State Circuits Conference in San Francisco, teams from MIT, Nvidia, and the Korea Advanced Institute of Science and Technology (KAIST) brought that goal closer. They showed off prototypes of low-power chips that are designed to run artificial neural networks that could, among other things, give smartphones a bit of a clue about what they are seeing and allow self-driving cars to predict pedestrians’ movements.

Until now, neural networks—learning systems that operate analogously to networks of connected brain cells—have been much too energy intensive to run on the mobile devices that would most benefit from artificial intelligence, like smartphones, small robots, and drones. The mobile AI chips could also improve the intelligence of self-driving cars without draining their batteries or compromising their fuel economy.

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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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