# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 3 Discussion and Conclusions
By integrating more photonic devices, the common power required for computing will be averaged, thereby improving the energy efficiency of optical computing and giving full play to the advantages of optical computing. Second, more functions should be realized with optical methods. Most optical CNN solutions primarily focus on the optical implementation of convolutions. Although there are related studies on the optical implementation of nonlinear activation and full connection [35; 38; 91; 113; 117; 118; 119; 120], these studies remain relatively limited and warrant further exploration. In particular, limited by the small matrix size, it is still a challenge to achieve large-scale full connections using optical methods. Implementing more functions using optical methods will be conducive to expanding the application field of optical computing and promoting the practical application of optical computing. Finally, the development of an in situ trainable arbitrary reconfigurable computing architecture is essential. At present, most optical CNN implementations adopt the offline training method, and the weight matrix is pretrained in the neural network simulation model. As a result, a deviation between the simulation model and the experimental system inevitably appears. In situ training, which updates the weight directly and performs the computation at the original place, offers a new form to accelerate the reconfiguration performance of the neural network and improve its precision. Several recently proposed in situ training schemes, such as physics-aware training [121], adaptive training [122], and other relevant methods [123; 124], have been successfully introduced to optical computing. These approaches can be effectively incorporated into the optical CNN framework. By incorporating in situ training, it is more convenient to reconstruct the network structure (such as changing the size and number of convolution kernels, changing the number of convolution layers, etc. ), and the influence of errors can be considered during the training process. In this way, the refactoring of more different, more complex classification tasks on the same hardware will become a reality, rather than the simple tasks that are fixed today (such as classifying handwritten digits). In summary, various optical methods have been used to conduct convolution operations with the benefit of high computing speed, low power consumption, and robust parallelism. Here, the review primarily introduces the recent advancements in optical CNNs, classifying them into four categories and summarizing their respective strengths and weaknesses. The challenges faced by optical CNNs are also summarized, and the future prospects of this technology are discussed. Next, the untapped potential of optical CNNs warrants further exploration, as they hold promise in shaping next-generation artificial neural network platforms. This research was funded in part by the National Natural Science Foundation of China, grant numbers 62235011, 61925505, and 62075212, and in part by the Youth Innovation Promotion Association of Chinese Academy of Sciences, grant number 2022111. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.