To counter the exponential growth of computing power requirements for machine learning, efficient sailing of the integrated photonic processors has become a fundamental issue to be addressed. However, it remains challenging to properly calibrate the circuit imperfections, such as fabrication errors and crosstalk originating from both thermal and electric effects, which drastically affects the performance as the circuit size becomes larger. We demonstrate a silicon-photonics 16×16 Clements-type photonic vector-matrix multiplier. The degradation of fidelity caused by crosstalk and fabrication error was successfully compensated using our proposed machine learning based tuning method and deterministic calibration. The first experimental 10-digit MNIST classification was performed, which defines the classification results directly corresponding to the optical output ports. Furthermore, we also fabricated an 8 8 MZI-mesh photonic processor based on the planar lightwave circuit (PLC) technique which can realize low wavelength dependence operation due to low fabrication errors. This structure achieves the efficient throughput due to the O(N2W) operation, where N and W denote the number of spatial and wavelength channels, respectively. A high fidelity of 0.99 at 1550 nm and >0.96 over the C band was achieved, demonstrating the feasibility of the matrix-matrix multiplication operation with a combination of MZI-mesh and WDM.
Photonic neural networks (PNNs) have been attracting intense attention owing to their low latency and low-power consumption. Among the PNNs, the optoelectronic recurrent neural network (RNN) enables low-power and high-speed time-series data processing using a compact loop structure. However, the RNN performance can be limited by the resistive-capacitive (RC) delay of optical-electrical-optical (OEO) converters. Here, we study in simulation an optoelectronic RNN equipped with OEO converters with RC delay. We confirm the correct RNN behavior even when RC delay is comparably large to the time interval of data. Our simulation results show that the optoelectronic RNN achieves 80% test accuracy for the sequential MNIST task and that the accuracy increases with RC delay when loop losses exist. Our analyses reveal that the accumulation of time-series data by RC delay does not degrade the RNN performance but rather can compensate for the degraded RNN performance due to loop losses.
KEYWORDS: Analog electronics, Silicon photonics, Nanophotonics, Calibration, Phase shifts, Matrices, Machine learning, Signal to noise ratio, Databases, Data modeling
We show our recent progress on a Clements-type16x16 on-chip matrix processor based on silicon photonics and a new type of electro-optic digital-to-analog converters (EO DACs) with a higher signal-to-noise ratio. For the former, we developed a machine-learning-based calibration technique that involves theoretical modeling with circuit parameters (loss, phase error, splitting ratio, and crosstalks), which is adequate to obtain better fidelity for large-scale imperfect interferometers. After the calibration, we demonstrated a 16x16 identity matrix and several permutation matrices with a high signal-to-noise ratio and a well-known MNIST database classification task. For the latter, we developed low-loss and wavelength insensitive EO DACs consisting of 1:1 Y splitters and phase modulators that are useful for DAC-less input units for photonic accelerators.
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