The advancement in the field of machine learning is inextricably linked with the concurrent progress in domain-specific hardware accelerators such as GPUs and TPUs. However, the rapidly growing computational demands necessitated by larger models and increased data have become a primary bottleneck in further advancing machine learning, especially in mobile and edge devices. Currently, the neuromorphic computing paradigm based on memristors presents a promising solution. In this study, we introduce a memristor-based MobileNetV3 neural network computing paradigm and provide an end-to-end framework for validation. The results demonstrate that this computing paradigm achieves over 90\% accuracy on the CIFAR-10 dataset while saving inference time and reducing energy consumption. With the successful development and verification of MobileNetV3, the potential for realizing more memristor-based neural networks using this computing paradigm and open-source framework has significantly increased. This progress sets a groundbreaking pathway for future deployment initiatives.

翻译：机器学习领域的进步与GPU、TPU等专用硬件加速器的同步发展密不可分。然而，大规模模型与海量数据带来的计算需求急剧增长，已成为制约机器学习进一步发展的主要瓶颈，尤其是在移动端与边缘设备领域。当前，基于忆阻器的神经形态计算范式展现出极具前景的解决方案。本研究提出了一种基于忆阻器的MobileNetV3神经网络计算范式，并构建了端到端验证框架。结果表明，该计算范式在CIFAR-10数据集上实现了超过90%的准确率，同时节省了推理时间并降低了能耗。随着MobileNetV3的成功开发与验证，利用该计算范式与开源框架实现更多基于忆阻器的神经网络的可能性显著提升。这一进展为未来的部署应用开创了突破性路径。