Memristor-Based MobileNetV3 Circuit Design for Image Classification
<|reference_start|>Memristor-Based MobileNetV3 Circuit Design for Image Classification: 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.<|reference_end|>
@article{li2024memristor-based,
    title={Memristor-Based MobileNetV3 Circuit Design for Image Classification},
    author={Jiale Li, Yulin Fu, Dongwei Yan, Sean Longyu Ma, Chiu-Wing Sham, Chong
  Fu},
    journal={arXiv preprint arXiv:2402.10512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10512},
    primaryClass={cs.AR}
}