The increasing computational demands of deep learning models pose significant challenges for edge devices. To address this, we propose a memristor-based circuit design for MobileNetV3, specifically for image classification tasks. Our design leverages the low power consumption and high integration density of memristors, making it suitable for edge computing. The architecture includes optimized memristive convolutional modules, batch normalization modules, activation function modules, global average pooling modules, and fully connected modules. Experimental results on the CIFAR-10 dataset show that our memristor-based MobileNetV3 achieves over 90% accuracy while significantly reducing inference time and energy consumption compared to traditional implementations. This work demonstrates the potential of memristor-based designs for efficient deployment of deep learning models in resource-constrained environments.

翻译：深度学习模型日益增长的计算需求对边缘设备提出了重大挑战。为此，我们提出了一种基于忆阻器的MobileNetV3电路设计，专门用于图像分类任务。该设计利用了忆阻器的低功耗和高集成密度特性，使其适用于边缘计算场景。架构包含优化的忆阻卷积模块、批量归一化模块、激活函数模块、全局平均池化模块以及全连接模块。在CIFAR-10数据集上的实验结果表明，与传统实现方案相比，基于忆阻器的MobileNetV3在保持90%以上准确率的同时，显著降低了推理时间和能耗。本工作证明了基于忆阻器的设计在资源受限环境中高效部署深度学习模型的潜力。