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.

翻译：光学神经网络（ONN）因其高并行性、低延迟和低能耗，成为下一代神经形态计算领域极具前景的硬件平台。然而，现有的集成光子张量核（PTC）需使用大量单操作数光学调制器进行信号和权重编码，导致在实现大规模张量运算时面临大面基成本和高传播损耗问题。本文提出一种基于定制多操作数光子器件（即多操作数光学神经元，MOON）的可扩展高效光学点积引擎。我们通过多操作数马赫-曾德尔干涉仪（MOMZI）在图像识别任务中实验验证了MOON的实用性。具体而言，基于MOMZI的ONN在采用4位电压控制精度下，街道视图门牌号（SVHN）识别数据集上实现了85.89%的测量准确率。此外，性能分析表明，基于128×128 MOMZI的PTC相比基于单操作数MZI的同类方案，在传播损耗、光学延迟和总器件占位面积上具有一至两个数量级的优势，且矩阵表达能力相当。