PIC: Hybrid Silicon Electronic-Photonic Integrated Neuromorphic Networks

Rochester Institute Of Tech, Rochester NY

Investigators

Abstract

Neuromorphic computing is a sub-field of artificial intelligence that implements physical architectures inspired by the learning processes in the brain. There have been significant efforts to realize neural network architectures using electronic integrated circuit technology. However, electronic-only hardware is not suitable for high bandwidth applications critical to a modern information world. In contrast, the internet is powered by photonic technologies (lasers, electro-optic modulator and photodetectors) because of light's high bandwidth, speed and low energy consumption. Consequently, this project aims to realize high performance neural networks that utilize light. These photonic neural networks will be integrated on a photonic chip in order to realize scalable and efficient architectures. However, in order to build neural networks that transcend today's state-of-art, it is necessary to also leverage electronics due to the challenges surrounding photonic memory and amplification, both of which are key to realizing a general purpose neural network. This hybrid approach, where electronics and photonics would be integrated together, enables the investigation of the broadest class of problems. In addition to these research aims, this project is an interdisciplinary activity that will provide technical training for future science and engineering professionals. There will be outreach activities that bring the research to K-12, undergraduate, and graduate students. Students from underrepresented backgrounds will be actively engaged by providing lab visits with hands-on activities. Lastly, the education initiatives of AIM Photonics Academy will be leveraged to disseminate the research in the project. Overall this project will impact the broader community with applications in autonomous systems, vision systems, information networks, cybersecurity, robotics and other high bandwidth applications. This project aims to address two fundamental questions, i) How can photonics maximize functionality in the compute domains?, ii) What neuromorphic algorithms can solve a broad class of problems using photonics? The overall goal of this project is to demonstrate hybrid silicon electronic-photonic integrated neuromorphic networks. The proposed paradigm leverages the power of optical interference to realize high performance neuromorphic computing networks. Photonic implementations of neural networks offer the inherent advantage that light can easily perform computational tasks that are traditionally challenging to do in electronic-only implementations (e.g. a Fourier transform can be done optically by simply passing light through a lens). The underlying integrated photonic-electronic network proposed here utilizes a Multimode interference coupler as a neural core (Neuro-MMI) in order to realize interference between multiple inputs and outputs in a compact footprint. The principal investigators propose to realize reconfigurability of the weights in the neural network wrapped around the MMI core. The Neuro-MMI core will be integrated with optoelectronic nonlinear thresholding circuits (along with electronic memory) to realize different classes of neural networks (feed forward neural networks and recurrent neural networks). Active Neuro-MMI's will be studied to realize on-chip learning and new learning rules will be investigated that are inherent for these topologies. Furthermore, the use of wavelength division multiplexing will be explored to achieve dense connectivity and parallelism in order to maximize the performance of the networks. One unique feature of the proposed hybrid photonic-electronic network is the reconfigurability to switch between feed forward and recurrent neural networks on a single chip. 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.