The optical neural network (ONN) is a promising hardware platform for
next-generation neuromorphic computing due to its high parallelism, low
latency, and low energy consumption. However, previous integrated photonic
tensor cores (PTCs) consume numerous single-operand optical modulators for
signal and weight encoding, leading to large area costs and high propagation
loss to implement large tensor operations. This work proposes a scalable and
efficient optical dot-product engine based on customized multi-operand photonic
devices, namely multi-operand optical neurons (MOON). We experimentally
demonstrate the utility of a MOON using a
multi-operand-Mach-Zehnder-interferometer (MOMZI) in image recognition tasks.
Specifically, our MOMZI-based ONN achieves a measured accuracy of 85.89% in the
street view house number (SVHN) recognition dataset with 4-bit voltage control
precision. Furthermore, our performance analysis reveals that a 128x128
MOMZI-based PTCs outperform their counterparts based on single-operand MZIs by
one to two order-of-magnitudes in propagation loss, optical delay, and total
device footprint, with comparable matrix expressivity.

通过使用多操作光神经元 (MOON) 和 Mach-Zehnder 干涉仪 (MOMZI) 构建的光学点积引擎，本文构建了基于 optoelectronic 混合计算的神经网络，并在街景房号识别数据集中实现了 85.89% 的分类精度，相较 传统的 单操作光调制器具有更少的传播损耗、更少的光学延时、更小的设备占地面积和可比的矩阵表达能力