Ríos Carlos, Youngblood Nathan, Cheng Zengguang, Le Gallo Manuel, Pernice Wolfram H P, Wright C David, Sebastian Abu, Bhaskaran Harish
Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK.
IBM Research-Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland.
Sci Adv. 2019 Feb 15;5(2):eaau5759. doi: 10.1126/sciadv.aau5759. eCollection 2019 Feb.
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, GeSbTe, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot / 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.
在诸如哺乳动物大脑等生物计算系统中,数据处理与存储的并置是常态。随着我们制造更好硬件能力的提升,除冯·诺依曼架构之外,新的计算范式正在被探索。集成光子电路是片上计算的一种有吸引力的解决方案,它可以利用光域增加的速度和带宽潜力,并且重要的是,无需电光转换。在此我们展示,我们能够将集成光学与并置的数据存储和处理相结合,以实现全光子内存计算。通过采用基于相变材料锗锑碲(GeSbTe)的非易失性光子元件,我们实现了直接标量和矩阵 - 向量乘法,具有新颖的单次操作和无漂移过程。携带光与物质相互作用信息的输出脉冲就是计算结果。我们的全光方法新颖、易于制造和操作,并为全光子计算机的发展奠定了基础。