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.
@article{arxiv.2402.10512,
title = {A Novel Computing Paradigm for MobileNetV3 using Memristor},
author = {Jiale Li and Zhihang Liu and Sean Longyu Ma and Chiu-Wing Sham and Chong Fu},
journal= {arXiv preprint arXiv:2402.10512},
year = {2025}
}
Comments
Published at the 2025 International Joint Conference on Neural Networks (IJCNN 2025)