Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-generation computing architectures. In particular, due to the recent outstanding achievements of artificial neural networks, there is a big interest in trying to improve their speed and energy efficiency by exploiting photonic-based hardware instead of electronic-based hardware. In this work we review the state-of-the-art of photonic artificial neural networks. We propose a taxonomy of the existing solutions (categorized into multilayer perceptrons, convolutional neural networks, spiking neural networks, and reservoir computing) with emphasis on proof-of-concept implementations. We also survey the specific approaches developed for training photonic neural networks. Finally we discuss the open challenges and highlight the most promising future research directions in this field. INDEX TERMS Artificial neural networks, neural network hardware, photonics, neuromorphic computing, photonic neural networks.

Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency.

Despite the increased exploration of machine learning (ML) techniques for the realization of autonomous optical networks, less attention has been paid to data quality, which is critical for ML performance. Failure management in optical networks using ML is constrained by the fact that some failures may occur more frequently than others, resulting in highly imbalanced datasets for the training of ML models. To address this limitation, a variational-autoencoder-based data augmentation technique is investigated in this paper, which can be used during data preprocessing to improve data quality. The synthetic data generated by the variational autoencoder are utilized to reduce imbalance in an experimental dataset used for training of neural networks (NNs) for failure management in optical networks. First, it is shown that, with a modified training dataset, the training time of NNs can be reduced. Reductions of up to 37.1% and 60.6% are achieved for failure detection and cause identification, respectively. Second, it is shown that improvement in the quality of the training dataset can reduce the computational complexity of NNs during the inference phase. As determined analytically, almost 68% reduction in computational complexity is achieved for the NN used for failure cause identification. Finally, data augmentation is shown to achieve improvement in classification accuracy. This work demonstrates improvement of up to 7.32%.

The characterization of a broadband low-loss 4 × 4 MZI-based reconfigurable linear optical processor is reported. The impact of MZI extinction ratio on the effective number of bits (ENOB) at the device output is also investigated.

We present a silicon photonic filter-based analog engine for computing dot products in convolutional neural networks. It shows a greater energy efficiency compared to electronic solutions with a limited bit resolution degradation of input signals.

The characterization of a broadband Si3N4 integrated linear optical processor operating in the C-band is reported. The impact of losses on the processor accuracy is discussed towards the photonic implementation of state-of-the-art neural networks.

No abstract

We present a reduced-precision integrated photonic electronic multiply-accumulate (MAC) neuron with ADC-embedded nonlinearity. The proposed device trades off speed with resolution, outperforming both analog and digital electronic solutions in terms of speed and energy consumption.