  High-resolution dense prediction enables many appealing real-world
applications, such as computational photography, autonomous driving, etc.
However, the vast computational cost makes deploying state-of-the-art
high-resolution dense prediction models on hardware devices difficult. This
work presents EfficientViT, a new family of high-resolution vision models with
novel multi-scale linear attention. Unlike prior high-resolution dense
prediction models that rely on heavy softmax attention, hardware-inefficient
large-kernel convolution, or complicated topology structure to obtain good
performances, our multi-scale linear attention achieves the global receptive
field and multi-scale learning (two desirable features for high-resolution
dense prediction) with only lightweight and hardware-efficient operations. As
such, EfficientViT delivers remarkable performance gains over previous
state-of-the-art models with significant speedup on diverse hardware platforms,
including mobile CPU, edge GPU, and cloud GPU. Without performance loss on
Cityscapes, our EfficientViT provides up to 13.9$\times$ and 6.2$\times$ GPU
latency reduction over SegFormer and SegNeXt, respectively. For
super-resolution, EfficientViT delivers up to 6.4x speedup over Restormer while
providing 0.11dB gain in PSNR. For Segment Anything, EfficientViT delivers
48.9x higher throughput on A100 GPU while achieving slightly better zero-shot
instance segmentation performance on COCO.
