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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Dawei Li

Samsung Research America


Xiaolong Wang

Samsung Research America


Deguang Kong

Samsung Research America


DOI:

10.1609/aaai.v32i1.11876


Abstract:

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 on top-5 accuracy in ImageNet. Furthermore, by combining with other model compression techniques, DeepRebirth offers an average of 106.3ms inference time on the CPU of Samsung Galaxy S5 with 86.5% top-5 accuracy, 14% faster than SqueezeNet which only has a top-5 accuracy of 80.5%.

Topics: AAAI

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HOW TO CITE:

Dawei Li||Xiaolong Wang||Deguang Kong DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Dawei Li||Xiaolong Wang||Deguang Kong DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices AAAI 2018, .

Dawei Li||Xiaolong Wang||Deguang Kong (2018). DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Dawei Li||Xiaolong Wang||Deguang Kong. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Dawei Li||Xiaolong Wang||Deguang Kong. 2018. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Dawei Li||Xiaolong Wang||Deguang Kong. (2018) "DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Dawei Li||Xiaolong Wang||Deguang Kong, "DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices", AAAI, p., 2018.

Dawei Li||Xiaolong Wang||Deguang Kong. "DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Dawei Li||Xiaolong Wang||Deguang Kong. "DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Dawei Li||Xiaolong Wang||Deguang Kong. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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