A Conceptual Study of Rapidly Reconfigurable and Scalable Optical Convolutional Neural Networks Based on Free-Space Optics Using a Smart Pixel Light Modulator

Smart-pixel-based optical convolutional neural network (SPOCNN) was proposed to improve kernel refresh rates in scalable optical convolutional neural networks by replacing the spatial light modulator with a smart pixel light modulator (SPLM), while maintaining benefits such as unlimited input node size, cascadability, and direct kernel representation. The fast updating capability and memory of SPLM enable real-time applications, including convolution with multiple kernel sets and difference mode. Simplifications using electrical fan-out reduced hardware complexity and costs. Smart-pixel-based bidirectional optical convolutional neural network (SPBOCNN), an evolution of SPOCNN, adopted bidirectional architecture and single lens-array optics, achieving a computational throughput of 8.3 × 10¹⁴ MAC/s with an SPLM resolution of 3840 × 2160. Further development led to two-mirror-like SPBOCNN (TML-SPBOCNN), which can emulate 2n layers using 2 physical layers, offering significant hardware savings despite increased time delay. TML-SPBOCNN was demonstrated for solving partial differential equations (PDEs), leveraging local interactions represented as a sequence of convolutions. These advancements establish SPOCNN and its derivatives as promising solutions for future convolutional neural network applications.