Optically-interconnected, hardware-efficient, electronic-photonic neural network using compact multi-operand photonic devices

Abstract

Optical neural network (ONN) is a promising platform for implementing deep learning tasks thanks to the critical features of light, such as high parallelism, low latency, and low power consumption. Previous ONN architectures are mainly composed of arrays of single-operand photonic devices such as Mach-Zehnder Interferometer (MZI) or microring resonator arrays. However, as the size of deep neural networks (DNNs) continues to grow, these ONNs will encounter unnecessary hardware costs, such as large chip areas and high power consumption. In this work, we devise several compact customized multi-operand active photonic components for tensor operations, for example, multi-operand ring modulators, to reduce the hardware cost of optical AI accelerators. Furthermore, we propose ONN architectures based on these multi-operand active photonic components. Compared to previous ONNs based on single-operand MZI or microring arrays, our work uses fewer optical and electrical components to implement matrix multiplications with comparable task performance. Finally, we experimentally demonstrate the utility of our proposed ONN architectures based on multi-operand photonic devices in several deep learning tasks.