Researchers at the Rochester Institute of Technology will use photonic integrated circuit technology to improve the processing speed and energy consumption of brain-inspired computing technique through a $422,733 grant from the National Science Foundation. PIC: Hybrid Silicon Electronic Photonic Integrated Neuromorphic Networks is a multiyear project to advance neuromorphic computing using photonic circuits. Neuromorphic computing, sometimes referred to as brain-inspired computing, is a subfield of artificial intelligence where the physical neural network architecture and its complex processing mechanisms are inspired by the learning mechanisms in the human brain. This type of architecture is currently developed using electronic integrated circuits, and the research team will be applying similar methods using photonic devices. The neuromorphic system will leverage the advantages of both electronics and photonics to achieve higher performance and speed for devices as well as lower energy consumption. Photonic implementations of neural networks offer an advantage because light can easily perform computational tasks that are traditionally challenging to do in electronic-only implementations. "Electronic-only hardware, such as CMOS – a widely used type of semiconductor – is not suitable for high-bandwidth applications critical to our modern information world,” said Stefan Preble, co-leader of the research team. “But the internet is powered by photonic technologies – lasers, electro-optic modulators, and photodetectors – because of light's high bandwidth, speed, and low-energy consumption. This project aims to realize high-performance neural networks using light.” Preble will be joined by Dhireesha Kudithipudi, professor of computer engineering and an expert in neuromorphic computing and artificial intelligence applications. In order to construct the neural networks for photonic chips, the team will build upon known capabilities of electronics to overcome the challenges of establishing better memory and amplification. This hybrid approach, where electronics and photonics would be integrated, enables the investigation of, and solutions for, the broadest class of problems in the evolution of improved photonic chips.