Information processing with large-scale optical integrated circuits

D. Kielpinski, R. Bose, J. Pelc, T. Vaerenbergh, G. Mendoza, N. Tezak, R. Beausoleil
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引用次数: 19

Abstract

Photonic integrated circuits (PICs) offer an enticing platform for further advances in computation. Photonic communications hardware is already widely used within datacenters and is now reaching into the board and chip level. This trend is driving the development of more complex PICs that are more tightly integrated into computing systems. This PIC technology could be attractive for building photonic computational accelerators and for incorporating all-optical signal processing tasks into photonic communications and sensing. At Hewlett Packard Labs, we are using a silicon photonics platform to build complex PICs with many hundreds of components, including nonlinear components. We use these PICs to test various approaches to photonic computation, including neuromorphic approaches as well as traditional logic circuits. For example, we are currently fabricating a circuit to solve the so-called Ising problem, a classic problem of solid-state physics that turns out to be equivalent to a number of combinatorial optimization problems. The circuit is closely related to Hopfield neural networks. In parallel, we are investigating PICs based on photonic crystals in an InGaAs platform. These PICs offer radically reduced power consumption compared to CMOS circuits, potentially consuming less than 1 fJ per elementary operation.
大规模光学集成电路的信息处理
光子集成电路(PICs)为进一步发展计算提供了一个诱人的平台。光子通信硬件已经广泛应用于数据中心,现在正在达到板和芯片的水平。这一趋势正在推动更复杂的pc的发展,这些pc与计算系统的集成更加紧密。这种PIC技术对于构建光子计算加速器和将全光信号处理任务整合到光子通信和传感中具有吸引力。在惠普实验室,我们正在使用硅光子学平台来构建包含数百个组件(包括非线性组件)的复杂pic。我们使用这些PICs来测试各种光子计算方法,包括神经形态方法以及传统的逻辑电路。例如,我们目前正在制造一种电路来解决所谓的伊辛问题,这是固态物理学的一个经典问题,结果相当于许多组合优化问题。该电路与Hopfield神经网络密切相关。同时,我们正在InGaAs平台上研究基于光子晶体的PICs。与CMOS电路相比,这些pic的功耗大大降低,每个基本操作的功耗可能低于1 fJ。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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