Illustration showing parallel convolutional processing using an integrated phonetic tensor core.
Machine learning at the speed of light: New paper demonstrates use of photonic structures for AI
As we enter the next chapter of the digital age, data traffic continues to grow exponentially. To further enhance artificial intelligence and machine learning, computers will need the ability to process vast amounts of data as quickly and as efficiently as possible.
Conventional computing methods are not up to the task, but in looking for a solution, researchers have seen the light—literally.
Light-based processors, called photonic processors, enable computers to complete complex calculations at incredible speeds. New research published this week in the journal Nature examines the potential of photonic processors for artificial intelligence applications. The results demonstrate for the first time that these devices can process information rapidly and in parallel, something that today’s electronic chips cannot do.
“Neural networks ‘learn’ by taking in huge sets of data and recognizing patterns through a series of algorithms,” explained Nathan Youngblood, assistant professor of electrical and computer engineering at the University of Pittsburgh Swanson School of Engineering and co-lead author. “This new processor would allow it to run multiple calculations at the same time, using different optical wavelengths for each calculation. The challenge we wanted to address is integration: How can we do computations using light in a way that’s scalable and efficient?”
The fast, efficient processing the researchers sought is ideal for applications like self-driving vehicles, which need to process the data they sense from multiple inputs as quickly as possible. Photonic processors can also support applications in cloud computing, medical imaging, and more.