Brückerhoff-Plückelmann Frank, Bente Ivonne, Wendland Daniel, Feldmann Johannes, Wright C David, Bhaskaran Harish, Pernice Wolfram
Department of Physics, University of Münster, CeNTech, Heisenberg Str. 11, 48155 Muenster, Germany.
University of Münster, Department of Physics, CeNTech, Heisenbergstraße 11, 48149 Münster, Germany.
Nanophotonics. 2022 Oct 28;12(5):819-825. doi: 10.1515/nanoph-2022-0441. eCollection 2023 Mar.
Integrated neuromorphic photonic circuits aim to power complex artificial neural networks (ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low loss of photonic structures. However, scaling photonic circuits to match the requirements of modern ANNs still remains challenging. In this perspective, we give an overview over the usual sizes of matrices processed in ANNs and compare them with the capability of existing photonic matrix processors. To address shortcomings of existing architectures, we propose a time multiplexed matrix processing scheme which virtually increases the size of a physical photonic crossbar array without requiring any additional electrical post-processing. We investigate the underlying process of time multiplexed incoherent optical accumulation and achieve accumulation accuracy of 98.9% with 1 ns pulses. Assuming state of the art active components and a reasonable crossbar array size, this processor architecture would enable matrix vector multiplications with 16,000 × 64 matrices all optically on an estimated area of 51.2 mm, while performing more than 110 trillion multiply and accumulate operations per second.
集成神经形态光子电路旨在通过利用光子结构的大带宽和低损耗，以高效节能的方式为复杂的人工神经网络（ANN）提供动力。然而，将光子电路扩展以满足现代ANN的要求仍然具有挑战性。从这个角度来看，我们概述了ANN中处理的矩阵的常见尺寸，并将它们与现有光子矩阵处理器的能力进行了比较。为了解决现有架构的缺点，我们提出了一种时分复用矩阵处理方案，该方案实际上增加了物理光子交叉阵列的尺寸，而无需任何额外的电后处理。我们研究了时分复用非相干光积累的基本过程，并使用1纳秒脉冲实现了98.9%的积累精度。假设采用先进的有源组件和合理的交叉阵列尺寸，这种处理器架构将能够在估计面积为51.2平方毫米的区域内，以全光方式对16000×64矩阵进行矩阵向量乘法运算，同时每秒执行超过110万亿次乘加运算。
