Brain-inspired memristive pooling method for enhanced edge computing

Abstract Edge deployment solutions based on convolutional neural networks (CNNs) have garnered significant attention because of their potential applications. However, traditional CNNs rely on pooling to reduce the feature size, leading to substantial information loss and reduced network robustness. Herein, we propose a more robust adaptive pooling network (APN) method implemented using memristor technology. Our method introduces an improved pooling layer that reduces input features to an arbitrary scale without compromising their importance. Different coupling coefficients of the pooling layer are stored as conductance values in arrays. We validate the proposed APN on generic datasets, demonstrating significant performance improvements over previously reported CNN architectures. Additionally, we evaluate the APN on a CAPTCHA recognition task with perturbations to assess network robustness. The results show that the APN achieves 92.6% accuracy in 4-digit CAPTCHA recognition and exhibits higher robustness. This brief presents a highly robust and novel scheme for edge computing using memristor technology.