利用光子集成电路实现高I/O效率的迭代矩阵求逆
I/O-efficient iterative matrix inversion with photonic integrated circuits
AI总结：
本文提出光子迭代处理器PIP，通过光域直接复用输入数据突破IO瓶颈，并在矩阵求逆、方程求解及MIMO预编码等任务中验证其显著效率优势。
摘要：
光子集成电路已被广泛探索用于光学处理，旨在突破数字电子技术的速度瓶颈。然而，输入/输出（IO）瓶颈仍是关键障碍之一。本文报道了一种面向矩阵求逆密集型应用的新型光子迭代处理器（PIP）。在光域中直接复用已输入的数据，释放了突破IO瓶颈的潜力。我们通过用于实值矩阵求逆和积分-微分方程求解的无损PIP，以及在片上集成光学环路、支持复值计算并实现1.2 ns净求逆时间的相干PIP，展示了显著的IO优势。此外，我们估计，在储层训练任务和MIMO预编码任务中，PIP的IO效率相比光子单程处理器和最先进的电子处理器至少提升一个数量级，表明PIP技术在实际应用中的巨大潜力。
英文摘要：
Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed bottleneck of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a novel photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted data in the optical domain unlocks the potential to break the IO bottleneck. We demonstrate notable IO advantages with a lossless PIP for real-valued matrix inversion and integral-differential equation solving, as well as a coherent PIP with optical loops integrated on-chip, enabling complex-valued computation and a net inversion time of 1.2 ns. Furthermore, we estimate at least an order of magnitude enhancement in IO efficiency of a PIP over photonic single-pass processors and the state-of-the-art electronic processors for reservoir training tasks and MIMO precoding tasks, indicating the huge potential of PIP technology in practical applications.
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