  With the success of Vision Transformers (ViTs) in computer vision tasks,
recent arts try to optimize the performance and complexity of ViTs to enable
efficient deployment on mobile devices. Multiple approaches are proposed to
accelerate attention mechanism, improve inefficient designs, or incorporate
mobile-friendly lightweight convolutions to form hybrid architectures. However,
ViT and its variants still have higher latency or considerably more parameters
than lightweight CNNs, even true for the years-old MobileNet. In practice,
latency and size are both crucial for efficient deployment on
resource-constraint hardware. In this work, we investigate a central question,
can transformer models run as fast as MobileNet and maintain a similar size? We
revisit the design choices of ViTs and propose a novel supernet with low
latency and high parameter efficiency. We further introduce a novel
fine-grained joint search strategy for transformer models that can find
efficient architectures by optimizing latency and number of parameters
simultaneously. The proposed models, EfficientFormerV2, achieve 3.5% higher
top-1 accuracy than MobileNetV2 on ImageNet-1K with similar latency and
parameters. This work demonstrate that properly designed and optimized vision
transformers can achieve high performance even with MobileNet-level size and
speed.
