MICROLENS OPTICAL CONVOLUTION FOR PHYSICAL MECHANISMS OF AI COMPUTING POWER ENHANCEMENT

Convolutional neural networks are widely used in tasks such as image recognition due to their excellent performance in two-dimensional data processing. To address the limited AI computing power under the traditional digital integrated circuit computing architecture, this study designs an optical convolution system based on a microlens array. Utilizing the ultra-high-speed and high-parallelism characteristics of optical computing, it achieves convolution computation based on the principles of geometrical optics. The system loads image information and convolution kernels using a spatial light modulator, segments the light field through a microlens array, modulates the segmented sub-regions in parallel using the convolution kernel, and finally converges through the imaging optical path to achieve summation, thus completing the convolution computation in optical space. This study presents the implementation principle of the optical convolution system based on a microlens array, designs the experimental optical path, and proposes implementation methods for key technologies such as system calibration, convolution kernel implementation, and image post-processing. Experimental results show that the system possesses basic imaging performance and the ability to implement complex feature operators such as Gaussian operator, Gradient operator, and Laplacian operator. Limited by device accuracy, the system still has errors caused by aberrations and diffraction. It can provide verification for the feasibility of optical convolution schemes based on geometrical optical architecture in image preprocessing tasks.