MobileNetV4 -- Universal Models for the Mobile Ecosystem
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
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.