Silicon and Neuromorphic Photonics
Neuromorphic processors are widely considered as one the next frontiers in computing. We are fundamentally investigating information processing by leveraging the strengths of nanophotonic (i.e., optical) physics and neuromorphic (i.e., brain-inspired) architectures, and emerging technology (silicon and graphene photonics) platforms. We seek to develop programmable photonic processors that have the potential to outperform state-of-the-art and future microelectronic processors in energy efficiency and computational speeds by seven- and four orders-of-magnitude, respectively. Our research: 1) explores energy-efficient photonic neurons with graphene-based electro-optic modulators (devices theme); 2) develop ultrafast, scalable, photonic neural network architectures in silicon photonics platforms (architectures theme); and 3) advance the understanding of processors for generalized neuromorphic computing tasks including deep learning and nonlinear optimization (applications theme).
Laser Dynamics and Networks
We are studying laser dynamics and developing photonic integrated circuits in III-V and hybrid silicon photonics platforms with networks of lasers for spike based information processing and chaos for optical secure communications. This paradigm takes advantage of ultrafast and low energy phenomena in photonic devices, has the potential for orders of magnitude performance improvement over current processing systems. Spike-based encoding scheme has both analog and digital qualities, and, via careful interleaving of the two representations, is potentially capable of achieving the high performance and low power characteristics of analog systems while simultaneously being cascadable and scalable. Our approach exploits both function-oriented models in optoelectronics and integrated interconnection strategies based on fiber optical networking.