Modern computing has been marked by a fascinating exponential trajectory since the invention of the transistor. Global networking and the Internet have also experienced an exponential growth thanks to optical fiber networking. Today, due to advances in integrated photonics, optical systems can overcome barriers in high-speed computing beyond the capabilities of semiconductor electronics.
Neuromorphic photonic computing draws from an improbable combination of three well-established research fields: silicon photonics, neuromorphic computing, and machine learning. This multidisciplinary field seeks to engineer efficient hardware that can emulate brain-inspired neural networks, which power most artificial intelligence applications today. By combining photonic devices and neuro-inspired architectures, this hardware can perform AI processing computations at very high speeds. The physics of photonic interactions is much more similar to the distributed nature of neural networks than the serial nature of digital processors.
In this Final Public Oral, I will share details of designs and experiments of a fully reconfigurable photonic neural network integrated on chip. I showcase the results of neural network experiments, which can be generalized to real-world applications in real-time computing, signal processing, optical communications, and predictive control. Going forward, I explore the next steps for engineering a neuromorphic photonic processor that can advance the state-of-the-art signal processing bandwidth by orders of magnitude.
Zoom Meeting ID: 913 0182 3172 Passcode: 867524