Mohamed Bin Zayed University of Artificial Intelligence<sup>1</sup>, University of California Merced<sup>2</sup>, Google Research<sup>3</sup>, Linkoping University<sup>4</sup>
Comparison with different self-attention modules. (a) is a typical self-attention. (b) is the transpose self-attention, where the self-attention operation is applied across channel feature dimensions (d×d) instead of the spatial dimension (n×n). (c) is the separable self-attention of MobileViT-v2, it uses element-wise operations to compute the context vector from the interactions of Q and K matrices. Then, the context vector is multiplied by V matrix to produce the final output. (d) Our proposed efficient additive self-attention. Here, the query matrix is multiplied by learnable weights and pooled to produce global queries. Then, the matrix K is element-wise multiplied by the broadcasted global queries, resulting the global context representation.
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<fontsize="+1">Abstract</font>
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Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8~ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2.
The latency reported in SwiftFormer for iPhone 14 (iOS 16) uses the benchmark tool from [XCode 14](https://developer.apple.com/videos/play/wwdc2022/10027/).
Community-driven results with [Samsung Galaxy S23 Ultra, with Qualcomm Snapdragon 8 Gen 2](https://www.qualcomm.com/snapdragon/device-finder/samsung-galaxy-s23-ultra):
1. [Export](https://github.com/escorciav/SwiftFormer/blob/main-v/export.py) & profiler results of [`SwiftFormer_L1`](./models/swiftformer.py):
| QNN | 2.16 | 2.17 | 2.18 |
| -------------- | -----| ----- | ------ |
| Latency (msec) | 2.63 | 2.26 | 2.43 |
2. [Export](https://github.com/escorciav/SwiftFormer/blob/main-v/export_block.py) & profiler results of SwiftFormerEncoder block:
| QNN | 2.16 | 2.17 | 2.18 |
| -------------- | -----| ----- | ------ |
| Latency (msec) | 2.17 | 1.69 | 1.7 |
Refer to script above for details of the input & block parameters.
❓ _Interested in reproducing the results above?_
Refer to [Issue #14](https://github.com/Amshaker/SwiftFormer/issues/14) for details about [exporting & profiling.](https://github.com/Amshaker/SwiftFormer/issues/14#issuecomment-1883351728)
Download and extract ImageNet train and val images from http://image-net.org. The training and validation data are expected to be in the `train` folder and `val` folder respectively:
Note: specify which model command you want to run in the script. To reproduce the results of the paper, use 16-GPU machine with batch-size of 128 or 8-GPU machine with batch size of 256. Auto Augmentation, CutMix, MixUp are disabled for SwiftFormer-XS, and CutMix, MixUp are disabled for SwiftFormer-S.
Our code base is based on [LeViT](https://github.com/facebookresearch/LeViT) and [EfficientFormer](https://github.com/snap-research/EfficientFormer) repositories. We thank authors for their open-source implementation.
## Our Related Works
- EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications, CADL'22, ECCV. [Paper](https://arxiv.org/abs/2206.10589) | [Code](https://github.com/mmaaz60/EdgeNeXt).
