Alex Yi, Univ. of Michigan, USA
Hank Smith, Massachusetts Institute of Technology, USA
Xingjun Wang, Peking University, China
Integrated Photonics is enabling artificial intelligence (AI). Combination of photonics and AI for photonics-enabled applications is an exciting new prospect. Artificial neural networks (ANNs) constitute the core information processing technology in the fields of artificial intelligence and machine learning, which have witnessed remarkable progress in recent years, and they are expected to be increasingly employed in real-world applications. In particular, integrated photonic devices using reservoirs based on physical phenomena has recently attracted increasing interest in many research areas. Various physical systems, substrates, and devices have been proposed for realizing ANNs. A motivation for physical implementation of reservoirs is to realize fast information processing integrated photonic devices with low learning cost. In contrast, physical implementation of reservoirs can be achieved using a variety of physical phenomena in the real world, because a mechanism for adaptive changes for training is not necessary. Actually, integrated photonics is one of the candidates of unconventional computing paradigms based on novel hardware. The session will cover the following topics: photonic neural net circuits componentry, photonic chip architectures, algorithms for photonic neural processors, demonstrated chips and achieved performance.