In-memory computing on a photonic platform

Science Advances
Carlos RíosHarish Bhaskaran

Abstract

Collocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge2Sb2Te5, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.

References

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Jun 26, 2018·Advanced Materials·Zengguang ChengHarish Bhaskaran

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Citations

Dec 11, 2019·Science Advances·Nikolaos FarmakidisHarish Bhaskaran
Mar 4, 2020·Optics Express·Hao HuB M A Rahman
Sep 13, 2019·Optics Express·Emanuele GemoC David Wright
Apr 2, 2020·Nature Nanotechnology·Abu SebastianEvangelos Eleftheriou
Jul 18, 2020·Nanotechnology·Karl BerggrenArijit Raychowdhury
Feb 2, 2021·Science Advances·Zengguang ChengHarish Bhaskaran
Apr 27, 2021·Advanced Science·Sae Byeok JoJeong Ho Cho
Jul 3, 2021·Materials·Irene VassaliniDomenico de Ceglia
Jul 24, 2021·Nature Communications·Meng-Cheng YenYa-Ju Lee
Jul 15, 2021·Nanoscale Horizons : the Home for Rapid Reports of Exceptional Significance in Nanoscience and Nanotechnolgy·Xuesong LiShuyu Zhang
Jan 22, 2022·Science Advances·Changming WuMo Li
Mar 6, 2020··Juejun HuLudmila J. Prokopeva
Oct 1, 2019··Peipeng XUZhuoran Fang

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Methods Mentioned

BETA
chip
electron-beam lithography

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