Optical/Photonics Computing System (OPTSys) Seminar Series
A series of talks from experts in optics and photonics
In a world where Moore's law is encountering the physical limits and electronic circuits, after six decades of development, face formidable barriers, the pursuit of novel computing paradigms has become paramount. Among these frontiers, optical and photonic systems have emerged as a driving force. Yet, they present a unique challenge – the complexity of understanding optical/photonic systems, governed by Maxwell's equations, surpasses that of electronic circuits.
This is where our seminar series steps in! We have assembled some of the brightest minds from both industry and academia to bring the cutting-edge field of optical/photonic systems to your doorstep. Whether you are an enthusiastic junior student seeking to explore the wonders of this domain or a seasoned faculty member aiming to delve deeper, our seminars offer an ideal platform. Expect dynamic discussions, inspiration, and the potential for fruitful collaborations. Join us in shaping the future of computing. The future has arrived, and it's illuminated by optics!
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Upcoming Seminar
Title: The challenging road towards an optical computing advantage for optimization and AI workloads
Abstract: Given the fast increases in computational requirements for AI workloads, which demand for tremendous energy-efficiency and throughput enhancements during the next decade, alternative ways to compute are getting traction again. In this talk, we will first give a literature overview of recent progress in the field of optical computing that is trying to address this need, and we will highlight remaining challenges and best practices when studying novel hardware proposals. Specifically, we will emphasize recent proposals for optical accelerators targeting AI and/or optimization workloads. We will explain how the heterogeneous IIIV-on-Silicon fabrication flow that Hewlett Packard Labs initially developed for O-band silicon photonic interconnects in HPC systems, can with minor modifications provide a promising platform for photonic neuromorphic computing, providing access to light sources, photodiodes, modulators, and non-volatile memory devices, providing an outlook to both inference and training capabilities of photonic matrix-vector product engines. Finally, we show in simulation how matrix decomposition techniques can be used to reduce the number of required on-chip devices when implementing weight matrices on-chip.
Biography: Thomas Van Vaerenbergh received the master's degree in applied physics and the Ph.D. degree in photonics from Ghent University, Ghent, Belgium, in 2010 and 2014, respectively. He was awarded the scientific prize Alcatel-Lucent Bell/FWO for his PhD thesis on all-optical spiking neurons in silicon photonics. In 2014, he joined the Large-Scale Integrated Photonics team in Hewlett Packard Labs, part of Hewlett Packard Enterprise (HPE), in Palo Alto, California. Since 2019, he is based in HPE Belgium and has been expanding HPE’s research activities related to photonics and AI in the EMEA region. His main research interests include analog photonic and electronic accelerators for combinatorial optimization and AI workloads, and inverse design of photonic devices and circuits based on physics-informed machine learning.
Date and Time: May 31, 2024, 10:00 am - 11:00 am (U.S. Eastern Time)
Zoom Link: https://mit.zoom.us/j/94224703097
Organizers
Acknowledgment
This seminar was made possible through the generous contributions of our presenters and the active participation of our audience. Particualrly, we would like to extend our gratitude to the following individuals for their invaluable help in refining this seminar and making it accessible to a wider audience: Hanrui Wang.