Harnessing noise in optical computing for AI
Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify.
In the near future, it’s predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries.
But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. It’s easy to see that this level of energy consumption is unsustainable.
A research team led by the University of Washington has developed new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. The research also addresses another challenge — the ‘noise’ inherent to optical computing that can interfere with computing precision.
In a new paper, published Jan. 21 in Science Advances, the team demonstrates an optical computing system for AI and machine learning that not only mitigates this noise but actually uses some of it as input to help enhance the creative output of the artificial neural network within the system.
“We’ve built an optical computer that is faster than a conventional digital computer,” said lead author Changming Wu, a UW doctoral student in electrical and computer engineering. “And also, this optical computer can create new things based on random inputs generated from the optical noise that most researchers tried to evade.”
Optical computing noise essentially comes from stray light particles, or photons, that originate from the operation of lasers within the device and background thermal radiation. To target noise, the researchers connected their optical computing core to a special type of machine learning network, called a Generative Adversarial Network. More