  Vision Transformers (ViT) have shown rapid progress in computer vision tasks,
achieving promising results on various benchmarks. However, due to the massive
number of parameters and model design, \textit{e.g.}, attention mechanism,
ViT-based models are generally times slower than lightweight convolutional
networks. Therefore, the deployment of ViT for real-time applications is
particularly challenging, especially on resource-constrained hardware such as
mobile devices. Recent efforts try to reduce the computation complexity of ViT
through network architecture search or hybrid design with MobileNet block, yet
the inference speed is still unsatisfactory. This leads to an important
question: can transformers run as fast as MobileNet while obtaining high
performance? To answer this, we first revisit the network architecture and
operators used in ViT-based models and identify inefficient designs. Then we
introduce a dimension-consistent pure transformer (without MobileNet blocks) as
a design paradigm. Finally, we perform latency-driven slimming to get a series
of final models dubbed EfficientFormer. Extensive experiments show the
superiority of EfficientFormer in performance and speed on mobile devices. Our
fastest model, EfficientFormer-L1, achieves $79.2\%$ top-1 accuracy on
ImageNet-1K with only $1.6$ ms inference latency on iPhone 12 (compiled with
CoreML), which runs as fast as MobileNetV2$\times 1.4$ ($1.6$ ms, $74.7\%$
top-1), and our largest model, EfficientFormer-L7, obtains $83.3\%$ accuracy
with only $7.0$ ms latency. Our work proves that properly designed transformers
can reach extremely low latency on mobile devices while maintaining high
performance.
