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Strategies for training optical neural networks

Deep learning has flourished in different areas in recent years, such as computer vision and natural language processing. However, with the end of Moore’s law, these applications that rely heavily on computing power are facing bottlenecks. Optical neural networks (ONNs) [1] use light to perform calculations [2,3] featuring high speed and low energy consumption, and they are widely regarded as the next-generation applicationspecific integrated circuit (ASIC) [4,5] for artificial intelligence. At present, ONNs have demonstrated many applications, such as vowel recognition and handwritten digit recognition. Compared with current mainstream deep learning applications, for example, end-to-end object detection [6], the tasks implemented on ONNs are very naive. One reason is that the number of neurons in current ONNs is much less than their electronic competitors. However, such limitation can be removed by novel architecture design and improvement of optoelectronic integration technology eventually. Another reason is that the strategies for training ONNs are still in a very early stage without a unified framework. Different from digital neural networks, the backpropagation method is not suitable for all ONNs because the gradient information is not easily available in some architectures [7]. To overcome this dilemma, three classes of training strategies are specifically designed. Compared to their conventional electrical counterparts, ONNs using these strategies have shown the comparable performance. The first class relies on fine tuning to map parameters from pre-trained models in-silico. Although these strategies can improve the inference performance, the ONNs fail to exploit the speed advantage due to the long training time caused by fabrication deviation and environmental fluctuation. Therefore, another class based on in-situ training is proposed. These strategies, directly training neural networks on the physical implementation, can adaptively find optimal parameters under system distortion and are more suitable for realistic application. However, restricted by approaches to obtain internal information from ONNs, in-situ training strategies (gradient-based and gradient-free) still suffer from a slow convergence rate for training large-scale ONNs. The third class proposes a hybrid in silico-in situ algorithm that utilizes backpropagation to the physical system. Although it shows potential to train physical neural networks (PNN), PNN can only be considered as an extra supplement