# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 3 Discussion and Conclusions  
As an emerging research direction, optical CNNs have garnered significant attention since their inception. Table 1 presents a comparison of four optical CNN schemes, highlighting their parallelism, computing speed, integration density, and reconfigurability. Diffraction-based optical CNNs exhibit advantages in terms of parallelism, computing speed, and scalability due to their utilization of spatial light. The presence of spatial light allows for a large number of neurons in each layer, facilitating the expansion of multiple channels and kernels in the spatial domain, thus enabling high parallelism. The abundance of neurons and high parallelism contributes to achieving high computing speeds. However, diffraction-based optical CNNs also suffer from notable disadvantages. The discrete components used make the system bulky, and attempts at integration result in performance degradation. Moreover, kernel-loading devices such as DOE and metamaterials are nearly impossible to reconfigure, while SLM and DMD devices have low data rates (typically \(\sim\)kHz). An interference-based optical CNN excels in reconfigurability. This scheme often utilizes MZI for kernel matrix loading, enabling rapid refresh rates in the range of tens of GHz. Despite the advantages of high-speed reconstruction offered by MZIs, their relatively large volume limits the integration density of interference-based schemes. Furthermore, the use of coherent light restricts the transmission of only one light at a time in the optical waveguide, thereby constraining parallelism and computing speed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Parallelism** & **Computing Speed** & **Integration Density** & **Reconfigurability** \\ \hline
**Diffraction** & high & high & low & low \\ \hline
**Interference** & low & medium & medium & high \\ \hline
**WDM** & high & high & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of optical CNN schemes. Figure 8: Optical CNN schemes based on tunable optical attenuation. (**a**) Optical CNN realization with acousto-optical modulator arrays [112]. (**b**) Integrated end-to-end photonic deep neural network (PDNN) [113]. (**a**) Reprinted with permission from Ref. [112] under a CC BY 4.0 license. (**b**) Reprinted with permission from Ref. [113]. 2022, Springer Nature. WDM-based optical CNN represents a promising and extensively researched solution. This scheme fully exploits the optical wavelength dimension, leading to high parallelism. The use of MRR as a wavelength-sensitive optical attenuator, with its radius as small as several micrometers and modulation rate reaching tens of GHz, further enhances computing speed, integration density, and reconfigurability. The optical CNN based on tunable optical attenuation depends on the specific characteristics of the optical attenuator used. Due to the absence of a unified conclusion, it is not listed in the table. Additionally, apart from the four types of optical CNNs discussed above, there are other noteworthy optical CNN solutions, such as photon frequency synthesis [115] and photodetectors with adjustable responsivity [116], which warrant further research. Presently, although optical CNNs exhibit advantages in terms of bandwidth, latency, and computational speed compared with electrical architectures, optical CNNs face challenges in surpassing the limitations of small realizable matrix sizes, a limited range of realizable functions, and low computing precision. Consequently, extensive efforts are required for optical CNNs to gain widespread usage. First, on-chip large-scale integration needs to be broken through. The reported on-chip integrated optical accelerated computing architectures only realize the integration of tens of thousands devices at present, which is far less than their electrical counterparts. In optical computing, the power required for calculations such as electro-optical conversion, photoelectric conversion, and analog-to-digital conversion remains basically unchanged.