# DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices

arXiv: https://arxiv.org/abs/1708.04728 · HTML: https://mlsanity.com/paper/1708.04728/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [What Your Features Reveal: Data-Efficient Black-Box Feature Inversion Attack for Split DNNs](https://mlsanity.com/paper/2511.15316/) (2025)
- [Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices](https://mlsanity.com/paper/2403.02870/) (2024)
- [Mobiprox: Supporting Dynamic Approximate Computing on Mobiles](https://mlsanity.com/paper/2303.11291/) (2023)
- [Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems](https://mlsanity.com/paper/2403.06352/) (2024)

## Abstract (arXiv)

Deploying deep neural networks on mobile devices is a challenging task. Current model compression methods such as matrix decomposition effectively reduce the deployed model size, but still cannot satisfy real-time processing requirement. This paper first discovers that the major obstacle is the excessive execution time of non-tensor layers such as pooling and normalization without tensor-like trainable parameters. This motivates us to design a novel acceleration framework: DeepRebirth through "slimming" existing consecutive and parallel non-tensor and tensor layers. The layer slimming is executed at different substructures: (a) streamline slimming by merging the consecutive non-tensor and tensor layer vertically; (b) branch slimming by merging non-tensor and tensor branches horizontally. The proposed optimization operations significantly accelerate the model execution and also greatly reduce the run-time memory cost since the slimmed model architecture contains less hidden layers. To maximally avoid accuracy loss, the parameters in new generated layers are learned with layer-wise fine-tuning based on both theoretical analysis and empirical verification. As observed in the experiment, DeepRebirth achieves more than 3x speed-up and 2.5x run-time memory saving on GoogLeNet with only 0.4% drop of top-5 accuracy on ImageNet. Furthermore, by combining with other model compression techniques, DeepRebirth offers an average of 65ms inference time on the CPU of Samsung Galaxy S6 with 86.5% top-5 accuracy, 14% faster than SqueezeNet which only has a top-5 accuracy of 80.5%.