# A Novel Computing Paradigm for MobileNetV3 using Memristor Source: [A Novel Computing Paradigm for MobileNetV3 using Memristor]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:26:29.946Z Authors: Jiale Li, Zhihang Liu, Sean Longyu Ma, Chiu-Wing Sham, Chong Fu Published: 2024-02-16T08:57:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()