DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices
Performing inference of Convolutional Neural Networks (CNNs) on Internet of Things (IoT) edge devices ensures both privacy of input data and possible run time reductions when compared to a cloud solution. As most edge devices are memory- and compute-constrained, they cannot store and execute complex...
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| Veröffentlicht in: | International journal of parallel programming Jg. 49; H. 4; S. 600 - 624 |
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| Abstract | Performing inference of Convolutional Neural Networks (CNNs) on Internet of Things (IoT) edge devices ensures both privacy of input data and possible run time reductions when compared to a cloud solution. As most edge devices are memory- and compute-constrained, they cannot store and execute complex CNNs. Partitioning and distributing layer information across multiple edge devices to reduce the amount of computation and data on each device presents a solution to this problem. In this article, we propose DeeperThings, an approach that supports a full distribution of CNN inference tasks by partitioning fully-connected as well as both feature- and weight-intensive convolutional layers. Additionally, we jointly optimize memory, computation and communication demands. This is achieved using techniques to combine both feature and weight partitioning with a communication-aware layer fusion method, enabling holistic optimization across layers. For a given number of edge devices, the schemes are applied jointly using Integer Linear Programming (ILP) formulations to minimize data exchanged between devices, to optimize run times and to find the entire model’s minimal memory footprint. Experimental results from a real-world hardware setup running four different CNN models confirm that the scheme is able to evenly balance the memory footprint between devices. For six devices on 100 Mbit/s connections the integration of layer fusion additionally leads to a reduction of communication demands by up to 28.8%. This results in run time speed-up of the inference task by up to 1.52x compared to layer partitioning without fusing. |
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| AbstractList | Performing inference of Convolutional Neural Networks (CNNs) on Internet of Things (IoT) edge devices ensures both privacy of input data and possible run time reductions when compared to a cloud solution. As most edge devices are memory- and compute-constrained, they cannot store and execute complex CNNs. Partitioning and distributing layer information across multiple edge devices to reduce the amount of computation and data on each device presents a solution to this problem. In this article, we propose DeeperThings, an approach that supports a full distribution of CNN inference tasks by partitioning fully-connected as well as both feature- and weight-intensive convolutional layers. Additionally, we jointly optimize memory, computation and communication demands. This is achieved using techniques to combine both feature and weight partitioning with a communication-aware layer fusion method, enabling holistic optimization across layers. For a given number of edge devices, the schemes are applied jointly using Integer Linear Programming (ILP) formulations to minimize data exchanged between devices, to optimize run times and to find the entire model’s minimal memory footprint. Experimental results from a real-world hardware setup running four different CNN models confirm that the scheme is able to evenly balance the memory footprint between devices. For six devices on 100 Mbit/s connections the integration of layer fusion additionally leads to a reduction of communication demands by up to 28.8%. This results in run time speed-up of the inference task by up to 1.52x compared to layer partitioning without fusing. |
| Author | Schlichtmann, Ulf Hoffman, Alexander Mueller-Gritschneder, Daniel Stahl, Rafael Gerstlauer, Andreas |
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| Cites_doi | 10.1109/TCAD.2018.2858384 10.1109/ICDCS.2017.226 10.5281/zenodo.3700700 10.1109/CVPR.2015.7298594 10.1109/ASPDAC.2015.7058993 10.1007/s11554-019-00938-y 10.1016/j.neunet.2019.04.021 10.1145/3093337.3037698 10.1109/CVPR.2015.7298965 10.3390/info10060191 10.1109/MICRO.2016.7783725 10.1109/CVPR.2017.690 10.23919/DATE.2017.7927211 10.1109/JIOT.2018.2886757 10.1109/GCWkshps45667.2019.9024610 10.1007/978-3-030-27562-4_6 10.1145/3065386 10.1145/2935643.2935650 10.3390/fi11100209 10.1109/JIOT.2019.2913162 10.1145/2994551.2994564 10.1109/CVPR.2016.435 |
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| References | KangYNeurosurgeon: collaborative intelligence between the cloud and mobile edgeACM SIGARCH Comput. Arch. News201745161562910.1145/3093337.3037698 Martins Campos de OliveiraFBorinEPartitioning convolutional neural networks to maximize the inference rate on constrained iot devicesFuture Internet2019111020910.3390/fi11100209 Motamedi, M., Fong, D., Ghiasi, S.: Fast and energy-efficient CNN inference on IoT devices. arXiv preprint arXiv:161107151 (2016) Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (2015) AyindeBOInancTZuradaJMRedundant feature pruning for accelerated inference in deep neural networksNeural Netw.201911814815810.1016/j.neunet.2019.04.021 Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (2017) Alwani, M., Chen, H., Ferdman, M., Milder, P.: Fused-layer CNN accelerators. In: IEEE/ACM International Symposium on Microarchitecture (2016) Iandola, FN., et al.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5 mb model size. arXiv preprint arXiv:160207360 (2016) johnjforrest, et al.: coin-or/cbc: Version 2.10.5. (2020) https://doi.org/10.5281/zenodo.3700700 ChenJiRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networksIEEE Internet Things J.2019647011702410.1109/JIOT.2019.2913162 Chien, SY., et al.: Distributed computing in IoT: System-on-a-chip for smart cameras as an example. In: Asia and South Pacific Design Automation Conference, IEEE (2015) ShengJComputation offloading strategy in mobile edge computingInformation201910619110.3390/info10060191 Lavin, A., Gray, S.: Fast algorithms for convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2016) Mao, J., et al.: MoDnn: Local distributed mobile computing system for deep neural network. In: Design, Automation & Test in Europe, IEEE (2017) Arredondo-Velázquez, M., et al.: A streaming architecture for convolutional neural networks based on layer operations chaining. J. Real Time Image Process. (2020) Redmon, J.: Darknet: open source neural networks in c. (2013–2016). http://pjreddie.com/darknet Teerapittayanon, S., McDanel, B., Kung, HT.: Distributed deep neural networks over the cloud, the edge and end devices. In: IEEE International Conference on Distributed Computing Systems (2017) Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2015) ZhaoZBarijoughKMGerstlauerADeepThings: Distributed adaptive deep learning inference on resource-constrained IoT edge clustersIEEE Trans. Comput. Aided Design Integr. Circuits Syst.2018372348235910.1109/TCAD.2018.2858384 KhelifiHNeurosurgeon: ccollaborative intelligence between the cloud and mobile edgeIEEE Commun. Lett.2018231615629 Perron, L., Furnon, V.: Or-tools. (2019). https://developers.google.com/optimization Bhattacharya, S., Lane, ND.: Sparsification and separation of deep learning layers for constrained resource inference on wearables. In: ACM Conference on Embedded Network Sensor Systems (2016) Huynh, LN., Balan, RK., Lee, Y.: Deepsense: A gpu-based deep convolutional neural network framework on commodity mobile devices. In: Workshop on Wearable Systems and Applications, ACM (2016) SahniYCaoJYangLData-aware task allocation for achieving low latency in collaborative edge computingIEEE Internet Things J.2018623512352410.1109/JIOT.2018.2886757 Bisschop, J.: AIMMS optimization modeling. Lulu. com (2006) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 (2014) KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksCommun. ACM20176061097110510.1145/3065386 Stahl, R., et al.: Fully distributed deep learning inference on resource-constrained edge devices. In: International Conference on Embedded Computer Systems, Springer (2019) Tu, Y., Lin, Y.: Deep neural network compression technique towards efficient digital signal modulation recognition in edge device. IEEE Access (2019) 712_CR19 712_CR7 712_CR8 712_CR17 712_CR9 712_CR15 712_CR16 712_CR14 712_CR10 Z Zhao (712_CR29) 2018; 37 Y Kang (712_CR11) 2017; 45 Y Sahni (712_CR22) 2018; 6 H Khelifi (712_CR12) 2018; 23 712_CR28 712_CR26 BO Ayinde (712_CR3) 2019; 118 712_CR27 712_CR24 712_CR25 A Krizhevsky (712_CR13) 2017; 60 712_CR20 712_CR21 J Chen (712_CR6) 2019; 6 F Martins Campos de Oliveira (712_CR18) 2019; 11 712_CR1 712_CR2 J Sheng (712_CR23) 2019; 10 712_CR4 712_CR5 |
| References_xml | – reference: Huynh, LN., Balan, RK., Lee, Y.: Deepsense: A gpu-based deep convolutional neural network framework on commodity mobile devices. In: Workshop on Wearable Systems and Applications, ACM (2016) – reference: Chien, SY., et al.: Distributed computing in IoT: System-on-a-chip for smart cameras as an example. In: Asia and South Pacific Design Automation Conference, IEEE (2015) – reference: Tu, Y., Lin, Y.: Deep neural network compression technique towards efficient digital signal modulation recognition in edge device. IEEE Access (2019) – reference: Mao, J., et al.: MoDnn: Local distributed mobile computing system for deep neural network. In: Design, Automation & Test in Europe, IEEE (2017) – reference: Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 (2014) – reference: SahniYCaoJYangLData-aware task allocation for achieving low latency in collaborative edge computingIEEE Internet Things J.2018623512352410.1109/JIOT.2018.2886757 – reference: ChenJiRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networksIEEE Internet Things J.2019647011702410.1109/JIOT.2019.2913162 – reference: Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (2017) – reference: Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (2015) – reference: Alwani, M., Chen, H., Ferdman, M., Milder, P.: Fused-layer CNN accelerators. In: IEEE/ACM International Symposium on Microarchitecture (2016) – reference: Bisschop, J.: AIMMS optimization modeling. Lulu. com (2006) – reference: Lavin, A., Gray, S.: Fast algorithms for convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2016) – reference: Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2015) – reference: ShengJComputation offloading strategy in mobile edge computingInformation201910619110.3390/info10060191 – reference: AyindeBOInancTZuradaJMRedundant feature pruning for accelerated inference in deep neural networksNeural Netw.201911814815810.1016/j.neunet.2019.04.021 – reference: Stahl, R., et al.: Fully distributed deep learning inference on resource-constrained edge devices. In: International Conference on Embedded Computer Systems, Springer (2019) – reference: johnjforrest, et al.: coin-or/cbc: Version 2.10.5. (2020) https://doi.org/10.5281/zenodo.3700700 – reference: Perron, L., Furnon, V.: Or-tools. (2019). https://developers.google.com/optimization/ – reference: Redmon, J.: Darknet: open source neural networks in c. (2013–2016). http://pjreddie.com/darknet/ – reference: Iandola, FN., et al.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5 mb model size. arXiv preprint arXiv:160207360 (2016) – reference: KangYNeurosurgeon: collaborative intelligence between the cloud and mobile edgeACM SIGARCH Comput. Arch. News201745161562910.1145/3093337.3037698 – reference: Martins Campos de OliveiraFBorinEPartitioning convolutional neural networks to maximize the inference rate on constrained iot devicesFuture Internet2019111020910.3390/fi11100209 – reference: ZhaoZBarijoughKMGerstlauerADeepThings: Distributed adaptive deep learning inference on resource-constrained IoT edge clustersIEEE Trans. Comput. Aided Design Integr. Circuits Syst.2018372348235910.1109/TCAD.2018.2858384 – reference: Arredondo-Velázquez, M., et al.: A streaming architecture for convolutional neural networks based on layer operations chaining. J. Real Time Image Process. (2020) – reference: Bhattacharya, S., Lane, ND.: Sparsification and separation of deep learning layers for constrained resource inference on wearables. In: ACM Conference on Embedded Network Sensor Systems (2016) – reference: KhelifiHNeurosurgeon: ccollaborative intelligence between the cloud and mobile edgeIEEE Commun. Lett.2018231615629 – reference: Motamedi, M., Fong, D., Ghiasi, S.: Fast and energy-efficient CNN inference on IoT devices. arXiv preprint arXiv:161107151 (2016) – reference: KrizhevskyASutskeverIHintonGEImagenet classification with deep convolutional neural networksCommun. ACM20176061097110510.1145/3065386 – reference: Teerapittayanon, S., McDanel, B., Kung, HT.: Distributed deep neural networks over the cloud, the edge and end devices. In: IEEE International Conference on Distributed Computing Systems (2017) – volume: 37 start-page: 2348 year: 2018 ident: 712_CR29 publication-title: IEEE Trans. Comput. Aided Design Integr. Circuits Syst. doi: 10.1109/TCAD.2018.2858384 – ident: 712_CR24 – ident: 712_CR27 doi: 10.1109/ICDCS.2017.226 – ident: 712_CR10 doi: 10.5281/zenodo.3700700 – volume: 23 start-page: 615 issue: 1 year: 2018 ident: 712_CR12 publication-title: IEEE Commun. Lett. – ident: 712_CR26 doi: 10.1109/CVPR.2015.7298594 – ident: 712_CR7 doi: 10.1109/ASPDAC.2015.7058993 – ident: 712_CR20 – ident: 712_CR2 doi: 10.1007/s11554-019-00938-y – volume: 118 start-page: 148 year: 2019 ident: 712_CR3 publication-title: Neural Netw. doi: 10.1016/j.neunet.2019.04.021 – volume: 45 start-page: 615 issue: 1 year: 2017 ident: 712_CR11 publication-title: ACM SIGARCH Comput. Arch. News doi: 10.1145/3093337.3037698 – ident: 712_CR15 doi: 10.1109/CVPR.2015.7298965 – volume: 10 start-page: 191 issue: 6 year: 2019 ident: 712_CR23 publication-title: Information doi: 10.3390/info10060191 – ident: 712_CR19 – ident: 712_CR1 doi: 10.1109/MICRO.2016.7783725 – ident: 712_CR21 doi: 10.1109/CVPR.2017.690 – ident: 712_CR9 – ident: 712_CR16 doi: 10.23919/DATE.2017.7927211 – volume: 6 start-page: 3512 issue: 2 year: 2018 ident: 712_CR22 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2886757 – ident: 712_CR28 doi: 10.1109/GCWkshps45667.2019.9024610 – ident: 712_CR17 – ident: 712_CR25 doi: 10.1007/978-3-030-27562-4_6 – volume: 60 start-page: 1097 issue: 6 year: 2017 ident: 712_CR13 publication-title: Commun. ACM doi: 10.1145/3065386 – ident: 712_CR5 – ident: 712_CR8 doi: 10.1145/2935643.2935650 – volume: 11 start-page: 209 issue: 10 year: 2019 ident: 712_CR18 publication-title: Future Internet doi: 10.3390/fi11100209 – volume: 6 start-page: 7011 issue: 4 year: 2019 ident: 712_CR6 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2913162 – ident: 712_CR4 doi: 10.1145/2994551.2994564 – ident: 712_CR14 doi: 10.1109/CVPR.2016.435 |
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| SubjectTerms | Artificial neural networks Communication Computation Computer Science Electronic devices Inference Integer programming Internet of Things Linear programming Memory devices Optimization Partitioning Processor Architectures Run time (computers) Software Engineering/Programming and Operating Systems Theory of Computation Weight |
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| Title | DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices |
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