Computational, photonic crossbar arrays for scalable and efficient matrix operations

Nathan Youngblood; Vivswan Shah; Sadra Rahimi Kari

doi:10.1117/12.2646996

13 March 2023 Computational, photonic crossbar arrays for scalable and efficient matrix operations

Nathan Youngblood, Vivswan Shah, Sadra Rahimi Kari

Proceedings Volume PC12426, Silicon Photonics XVIII; PC1242602 (2023) https://doi.org/10.1117/12.2646996
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Advances in the field of deep learning have been thrilling to witness but come with an increasingly unsustainable appetite for computing resources. Thus, the generality and accuracy of deep learning is also its Achilles’ heel. Novel approaches to computation are therefore needed to address the slowing growth in compute performance and efficiency of electronic hardware in order to keep pace with the rapid advances in deep learning innovation. In this talk, I will present two complementary computing approaches which leverage photonic crossbar arrays and phase-change materials to perform low latency and high efficiency matrix operations for applications in deep learning.

Conference Presentation

Citation Download Citation

Nathan Youngblood, Vivswan Shah, and Sadra Rahimi Kari "Computational, photonic crossbar arrays for scalable and efficient matrix operations", Proc. SPIE PC12426, Silicon Photonics XVIII, PC1242602 (13 March 2023); https://doi.org/10.1117/12.2646996

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