MobileNetV4 -- Universal Models for the Mobile Ecosystem
<|reference_start|>MobileNetV4 -- Universal Models for the Mobile Ecosystem: We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Depthwise (ExtraDW) variant. Alongside UIB, we present Mobile MQA, an attention block tailored for mobile accelerators, delivering a significant 39% speedup. An optimized neural architecture search (NAS) recipe is also introduced which improves MNv4 search effectiveness. The integration of UIB, Mobile MQA and the refined NAS recipe results in a new suite of MNv4 models that are mostly Pareto optimal across mobile CPUs, DSPs, GPUs, as well as specialized accelerators like Apple Neural Engine and Google Pixel EdgeTPU - a characteristic not found in any other models tested. Finally, to further boost accuracy, we introduce a novel distillation technique. Enhanced by this technique, our MNv4-Hybrid-Large model delivers 87% ImageNet-1K accuracy, with a Pixel 8 EdgeTPU runtime of just 3.8ms.<|reference_end|>
@article{qin2024mobilenetv4,
    title={MobileNetV4 -- Universal Models for the Mobile Ecosystem},
    author={Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin
  Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, Vaibhav
  Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard},
    journal={arXiv preprint arXiv:2404.10518},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10518},
    primaryClass={cs.CV}
}