The team, led by professor Gordon Wetzstein, is addressing the challenge of autonomous vehicles and aerial drones relying on large, energy intensive computers to process images. They have joined two types of computers: optical and electrical, to create a hybrid machine that can analyze images with far less computation and energy.
The result is profoundly fewer calculations, fewer calls to memory and far less time to complete the process. Having leapfrogged these preprocessing steps, the remaining analysis proceeds to the digital computer layer with a considerable head start.
"Millions of calculations are circumvented and it all happens at the speed of light," reports Gordon Wetzstein. "Some future version of our system would be especially useful in rapid decision-making applications, like autonomous vehicles."
In addition to shrinking the prototype, Wetzstein, Chang and colleagues at the Stanford Computational Imaging Lab are now looking at ways to make the optical component do even more of the preprocessing. Eventually, their smaller, faster technology could replace the trunk-size computers that now help cars, drones and other technologies learn to recognize the world around them.
Their work was published in Nature Scientific Reports, "Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification", in August.
Excerpted from The Stanford News, "Stanford engineers create new AI camera for faster, more efficient image classification", August 17, 2018