Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
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Abstract (arXiv)
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter servers with powerful CPUs and GPUs, modern smartphones consist of a diverse collection of specialized cores following a system-on-a-chip (SoC) architecture that together perform a variety of tasks. We observe that training DNNs on a smartphone SoC without carefully considering its resource constraints can not only lead to suboptimal training performance but significantly affect user experience as well. In this paper, we present Swan, a neural engine to optimize DNN training on smartphone SoCs without hurting user experience. Extensive large-scale evaluations show that Swan can improve performance by 1.2 - 23.3x over the state-of-the-art.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 9 Jun 2022.
Builds on
22 of 38 references.
References · 22
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
- 2020Adaptive Federated Optimizationcited by 1,046
- 2019Towards Federated Learning at Scale: System Designcited by 832
- 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
- 2020An Efficient Framework for Clustered Federated Learningcited by 511
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
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- 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
- 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
- 2018A generic framework for privacy preserving deep learningcited by 103
- 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
- 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
- 2018Focus: Querying Large Video Datasets with Low Latency and Low Costcited by 46
- 2019Federated Heavy Hitters Discovery with Differential Privacycited by 32
- 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
- 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
- 2018Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutionscited by 6
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.