Abstract: Convolution lies at the cornerstone of artificial intelligence and represents a computationally intensive step in convolutional neural networks. However, the hardware performance using digital electronics for such convolution operations is constrained by low-speed operation, high-power consumption, and poor scalability to large data. The concept of synthetic dimensions points to new avenues for manipulating the properties of light. In this talk, we theoretically introduced and experimentally demonstrated a multi-dimensional convolution processor to achieve arbitrary convolution kernels in the synthetic frequency dimension. This approach points to using compact and reconfigurable integrated photonic circuits to improve machine learning hardware for state-of-the-art artificial intelligence performances.
Bio: Dr. Lingling Fan is a Postdoctoral Associate at Massachusetts Institute of Technology (MIT), with a joint appointment between Computer Science and Artificial Intelligence Laboratory (CSAIL) and Research Laboratory of Electronics (RLE), advised by Dirk Englund and Manya Ghobadi. She received a Ph.D. degree in Electrical Engineering at Stanford University advised by Shanhui Fan and she did her undergraduate research at Yale University, Applied Physics advised by Owen Miller. She was a research intern at Google LLC in 2022 summer working on chip designs. She has published over 30 papers in peer-reviewed journals and conference venues, including Nature Portfolio, American Association for the Advancement of Science, American Physical Society, Optical Society of America, and American Chemical Society. She has given several invited talks at Google Research, Boston University, Nokia Bell Labs, etc., and her research has translated to two U.S. patents and influenced emerging industry products such as Google TPU. She is a recipient of an Engineering Fellowship from Stanford University (2018), a CLEO presenter award (2020), a DARE fellowship finalist (2021), an EECS Rising Star award (2022), and a travel grant from ACM SIGCOMM (2023). Her research interests lie primarily in nanoscale and quantum photonic engineering for efficient computation hardware and sustainable energy.