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Frontiers of Optoelectronics volume 15, Article number: 15 (2022 ) Cite this article

As an important computing operation, photonic matrix–vector multiplication is widely used in photonic neutral networks and signal processing. However, conventional incoherent matrix–vector multiplication focuses on real-valued operations, which cannot work well in complex-valued neural networks and discrete Fourier transform. In this paper, we propose a systematic solution to extend the matrix computation of microring arrays from the real-valued field to the complex-valued field, and from small-scale (i.e., 4 × 4) to large-scale matrix computation (i.e., 16 × 16). Combining matrix decomposition and matrix partition, our photonic complex matrix–vector multiplier chip can support arbitrary large-scale and complex-valued matrix computation. We further demonstrate Walsh-Hardmard transform, discrete cosine transform, discrete Fourier transform, and image convolutional processing. Our scheme provides a path towards breaking the limits of complex-valued computing accelerator in conventional incoherent optical architecture. More importantly, our results reveal that an integrated photonic platform is of huge potential for large-scale, complex-valued, artificial intelligence computing and signal processing.

With the rapid advancement of technology in recent decades, there is a growing demand for large-capacity, high-speed computing over traditional computing. This is especially seen in the field of convolutional processing, a computationally intensive operation in electronics that occupies over 80% of the total processing time for image processing [1,2,3]. Optical computing has the ability of parallel processing with wavelength division multiplexing (WDM) due to its intrinsic high speed and low power consumption, thus has been proposed as a promising candidate for mass data processing [4]. Matrix multiplication is the kernel and most common operation in artificial intelligence (AI). It is widely used in artificial neutral networks (ANNs), which have been universally applied in signal processing, imaging recognition, voice recognition, real-time video analysis, and autonomous driving [5, 6]. The optical neural networks (ONNs) can improve the computation speed by several orders of magnitude. For example, a photonic convolutional accelerator comprised of soliton microcombs could carry out up to 10 trillion operations per second [7]. In addition, phase-change material (PCM) has been employed in non-volatile memory storage in optical computing to reduce the energy consumption of optical-electrical conversion during weight data refreshing [8,9,10,11]. Recently, an integrated photonic hardware accelerator has successfully executed \({10}^{12}\) multiply-accumulate operations per second by combining phase-change-material memory and soliton microcombs [9].

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