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Minisymposium Presentation
Integrated Photonics for High-Speed Neuromorphic Processing
Presenter
Nicola Andriolli received the Laurea degree in telecommunications engineering from the University of Pisa in 2002, and the Diploma and Ph.D. degrees from Scuola Superiore Sant’Anna, Pisa, in 2003 and 2006, respectively. He was a Visiting Student at DTU, Copenhagen, Denmark and a Guest Researcher at NICT, Tokyo, Japan. In 2007-2019 he was an Assistant Professor at Scuola Superiore Sant’Anna, in 2019-2023 he was with the National Research Council of Italy as a Researcher and then Senior Researcher at the Institute of Electronics, Information Engineering and Telecommunications (CNR-IEIIT). Since 2024 he is an Associate Professor in Telecommunications at the University of Pisa at the Department of Information Engineering.His background encompasses the design and performance analysis of optical circuit-switched and packet-switched networks and nodes. His research interests include photonic integration technologies for telecom, datacom and computing applications. His work has encompassed optical processing and optical interconnection network architectures and scheduling, and more recently, he have been investigating integrated transceivers, comb sources, as well as architectures and subsystems for photonic neural networks.He has authored over 200 publications in international journals and conferences, contributed to one IETF RFC, and filed 11 patents. Additionally, he has served as a Technical Program Committee Member for several international conferences (ICC, GLOBECOM, ECOC, EuCNC, IPR, PSC), worked as an Associate Editor of IEEE Access, and he is currently an Associate Editor of IEEE Photonics Journal.
Description
Although modern optical communications typically rely on digital signals, analog photonics has been an active field of research over the last decades. In parallel, photonics for computing experienced an intermittent research interest. Recent breakthroughs in artificial intelligence have sparked attention in novel computational frameworks, reigniting interest in analog photonic computing.This talk explores the potential of integrated photonics to realize analog photonic accelerators, in particular for machine learning applications. Various integrated photonic devices and system architectures are analyzed, exploiting wavelength division multiplexing for weight banks, coherent photonic crossbar arrays, and photonic-electronic multiply-accumulate neurons. Each architecture offers different advantages and trade-offs in terms of speed, resolution, power consumption, and footprint.Focusing on the latter architecture, different photonic integration technologies, including silicon-on-insulator, lithium niobate-on-insulator, and indium phosphide, are reviewed, the results of experimental implementations and the outstanding challenges are discussed, showcasing future prospects in the field of high-speed neuromorphic photonics.