Collaborative Research: CNS Core: Medium: TeTON: A Testbed and a Toolkit for Expediting Investigation of and Accelerating Advancements in All-Optical Neural Networks

In a brain, billions of neurons continuously collect electrical signals from one another and through natural non-linear combination of these signals human beings are able to process information (visual, audio, tactile, etc.), learn how to recognize patterns, and make quick decisions. These interconnected neurons form what is commonly known as a neural network.

This project aims to develop an all-optical (artificial) neural network (AONN) testbed and related software toolkit for expediting and accelerating advancements in optical artificial intelligence, including a programmable multi-layer AONN hardware with automatic machine learning capability and an open-source software package for public education. The AONN testbed builds upon free-space Fourier optics and ultra-low light level nonlinear optics, which can efficiently recreate with photons functionalities similar to those that are performed in the human brain.

Making use of the electromagnetic wave nature of light the resulting AONN is expected to run much faster and consume significantly less energy when compared to the more conventional software and custom electronic-based artificial neural networks that have been applied to realize artificial intelligence (AI) so far.

Discovering how to best and most effectively implement AONN solutions is going to change the way in which AI is achieved. An artificial brain that can think at the speed-of-light can pave the way to the realization of a number of new and currently unthinkable real-time applications, like for example inspecting the quality of a large number of manufactured objects and providing virtually instantaneous feedback to the production plant in the event that swift production adjustments are required.

Being able to quickly recognize patterns and their anomalies using AONN will find many other applications in the fields of medicine, education, manufacturing, transportation, agriculture, natural science, etc. The following example may illustrate the potential advantages of implementing and using AONN. Each video frame generated by a camera observing an athlete or a patient going though rehabilitation while performing specific tasks could be individually analyzed by AONN in less than one millisecond.

Improper or suboptimal body motions would be readily detected by AONN, hence providing instantaneous feedback to the athlete or patient who in turn could continuously correct their posture to achieve improved outcomes. This project supports career development of the undergraduate and graduate students, who work together on this cross-disciplinary project.

Courses and outreach materials are developed at the University of Texas at Dallas (UTD) and the University of Kansas (KU) and shared with the public, with an emphasis on engaging with students from underrepresented communities and contributing to future student diversity in STEM disciplines.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.