survey

Machine Learning at the Network Edge: A Survey

Authors:

M. G. Sarwar Murshed,

Christopher Murphy,

Ganesh Ananthanarayanan, and

Faraz HussainAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 8

Article No.: 170, Pages 1 - 37

https://doi.org/10.1145/3469029

Published: 04 October 2021 Publication History

Abstract

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

References

[1]

Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6 (2018), 52138–52160.

[2]

Adafruit. 2019. Micro Speech Demo. Retrieved from https://learn.adafruit.com/tensorflow-lite-for-edgebadge-kit-quickstart/micro-speech-demo.

[3]

M. Ali, A. Anjum, M. U. Yaseen, A. R. Zamani, D. Balouek-Thomert, O. Rana, and M. Parashar. 2018. Edge enhanced deep learning system for large-scale video stream analytics. In IEEE 2nd International Conference on Fog and Edge Computing (ICFEC). 1–10.

[4]

Alasdair Allan. 2018. Deep Learning at the Edge on an Arm Cortex-powered Camera Board. Retrieved from https://blog.hackster.io/deep-learning-at-the-edge-on-an-arm-cortex-powered-camera-board-3ca16eb60ef7.

[5]

Alasdair Allan. 2019. Benchmarking Edge Computing. Retrieved from https://medium.com/@aallan/benchmarking-edge-computing-ce3f13942245.

[6]

Alasdair Allan. 2019. Benchmarking the Xnor AI2GO Platform on the Raspberry Pi. Retrieved from https://blog.hackster.io/benchmarking-the-xnor-ai2go-platform-on-the-raspberry-pi-628a82af8aea.

[7]

Alasdair Allan. 2019. Hands-on with the SmartEdge Agile. Retrieved from https://blog.hackster.io/hands-on-with-the-smartedge-agile-b7b7f02b5d4b.

[8]

Alasdair Allan. 2019. Measuring Machine Learning. Retrieved from https://towardsdatascience.com/measuring-machine-learning-945a47bd3750.

[9]

G. Ananthanarayanan, P. Bahl, P. Bodík, K. Chintalapudi, M. Philipose, L. Ravindranath, and S. Sinha. 2017. Real-time video analytics: The killer app for edge computing. Computer 50, 10 (2017), 58–67.

Digital Library

[10]

Ganesh Ananthanarayanan, Victor Bahl, Landon Cox, Alex Crown, Shadi Nogbahi, and Yuanchao Shu. 2019. Video analytics—Killer app for edge computing. In 17th International Conference on Mobile Systems, Applications, and Services (MobiSys’19). ACM, New York, NY, 695–696.

Digital Library

[11]

Andrej Karpathy. 2019. PyTorch at Tesla. Retrieved from https://www.youtube.com/watch?v=oBklltKXtDE.

[12]

ARM Limited. Machine Learning ARM ML Processor. Retrieved on July 25, 2021 from https://developer.arm.com/ip-products/processors/machine-learning.

[13]

Asha Barbaschow. 2018. VMware looking towards IoT and the edge. Retrieved from https://www.zdnet.com/article/vmware-looking-towards-iot-and-the-edge/.

[14]

M. Barnell, C. Raymond, C. Capraro, D. Isereau, C. Cicotta, and N. Stokes. 2018. High-performance computing (HPC) and machine learning demonstrated in flight using Agile Condor. In IEEE High Performance Extreme Computing Conference (HPEC). 1–4.

[15]

B. Barry, C. Brick, F. Connor, D. Donohoe, D. Moloney, R. Richmond, M. O’Riordan, and V. Toma. 2015. Always-on vision processing unit for mobile applications. IEEE Micro 35, 2 (Mar. 2015), 56–66.

Digital Library

[16]

Sourav Bhattacharya and Nicholas D. Lane. 2016. Sparsification and separation of deep learning layers for constrained resource inference on wearables. In 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (SenSys’16). ACM, New York, NY, 176–189.

Digital Library

[17]

Sumon Biswas and Hridesh Rajan. 2020. Do the machine learning models on a crowd sourced platform exhibit bias? An empirical study on model fairness. In 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.

Digital Library

[18]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In ACM SIGSAC Conference on Computer and Communications Security. 1175–1191.

Digital Library

[19]

Andrew A. Borkowski, Catherine P. Wilson, Steven A. Borkowski, Lauren A. Deland, and Stephen M. Mastorides. 2019. Using Apple machine learning algorithms to detect and subclassify non-small cell lung cancer. Arxiv E-prints 1808.08230 (January 2019).

[20]

Brandon Butler. 2017. What is edge computing and how it’s changing the network. Network World (2017).

[21]

S. Cass. 2019. Taking AI to the edge: Google’s TPU now comes in a maker-friendly package. IEEE Spectrum 56, 5 (May 2019), 16–17.

[22]

W. Chang, L. Chen, and K. Su. 2019. DeepCrash: A deep learning-based internet of vehicles system for head-on and single-vehicle accident detection with emergency notification. IEEE Access 7 (2019), 148163–148175.

[23]

X. Chang, W. Li, C. Xia, J. Ma, J. Cao, S. U. Khan, and A. Y. Zomaya. 2018. From insight to impact: Building a sustainable edge computing platform for smart homes. In IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). 928–936.

[24]

G. Chen, C. Parada, and G. Heigold. 2014. Small-footprint keyword spotting using deep neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4087–4091.

[25]

J. Chen and X. Ran. 2019. Deep learning with edge computing: A review. Proc. IEEE 107, 8 (Aug. 2019), 1655–1674.

[26]

Min Chen, Yuanwen Tian, Giancarlo Fortino, Jing Zhang, and Iztok Humar. 2018. Cognitive internet of vehicles. Comput. Commun. 120 (2018), 58–70.

Digital Library

[27]

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI’18). USENIX Association, 578–594. https://www.usenix.org/conference/osdi18/presentation/chen.

Digital Library

[28]

Y. Chen, A. Wu, M. A. Bayoumi, and F. Koushanfar. 2013. Editorial low-power, intelligent, and secure solutions for realization of internet of things. IEEE J. Emerg. Select. Topics Circ. Syst. 3, 1 (Mar. 2013), 1–4.

[29]

Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017. A survey of model compression and acceleration for deep neural networks. Arxiv E-prints 1710.09282 (2017).

[30]

Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, and Marco Pavone. 2021. Network offloading policies for cloud robotics: a learning-based approach. Autonomous Robots (2021), 1–16. https://doi.org/10.1007/s10514-021-09987-4

[31]

Tejalal Choudhary, Vipul Mishra, Anurag Goswami, and Jagannathan Sarangapani. 2020. A comprehensive survey 1109 on model compression and acceleration. Artif. Intell. Rev. (2020), 1–43.

[32]

Christine Long. 2019. BeagleBone AI Makes a Sneak Preview. Retrieved from https://beagleboard.org/blog/2019-05-16-beaglebone-ai-preview.

[33]

N. Curukogle and B. M. Ozyildirim. 2018. Deep learning on mobile systems. In Innovations in Intelligent Systems and Applications Conference (ASYU). 1–4.

[34]

A. Das, M. Degeling, X. Wang, J. Wang, N. Sadeh, and M. Satyanarayanan. 2017. Assisting users in a world full of cameras: A privacy-aware infrastructure for computer vision applications. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1387–1396.

[35]

Rustem Dautov, Salvatore Distefano, Dario Bruneo, Francesco Longo, Giovanni Merlino, Antonio Puliafito, and Rajkumar Buyya. 2018. Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms. Softw., Pract. Exper. 48 (2018), 1475–1492.

[36]

G. Ditzler, M. Roveri, C. Alippi, and R. Polikar. 2015. Learning in nonstationary environments: A survey. IEEE Comput. Intell. Mag. 10, 4 (2015), 12–25.

Digital Library

[37]

Utsav Drolia, Katherine Guo, and Priya Narasimhan. 2017. Precog: Prefetching for image recognition applications at the edge. In 2nd ACM/IEEE Symposium on Edge Computing (SEC’17). ACM, New York, NY.

Digital Library

[38]

SparkFun Electronics. SparkFun Edge Hookup Guide. Retreived July 25, 2021 from https://learn.sparkfun.com/tutorials/sparkfun-edge-hookup-guide/all.

[39]

A. R. Elias, N. Golubovic, C. Krintz, and R. Wolski. 2017. Where’s the bear?—Automating wildlife image processing using IoT and edge cloud systems. In IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation (IoTDI). 247–258.

Digital Library

[40]

E. Ezra Tsur, E. Madar, and N. Danan. 2018. Code generation of graph-based vision processing for multiple CUDA cores SoC Jetson TX. In IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC). 1–7.

[41]

Z. Feng, S. George, J. Harkes, P. Pillai, R. Klatzky, and M. Satyanarayanan. 2018. Edge-based discovery of training data for machine learning. In IEEE/ACM Symposium on Edge Computing (SEC). 145–158.

[42]

E. Flamand, D. Rossi, F. Conti, I. Loi, A. Pullini, F. Rotenberg, and L. Benini. 2018. GAP-8: A RISC-V SoC for AI at the edge of the IoT. In IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP). 1–4.

[43]

David Floyer. 2015. The Vital Role of Edge Computing in the Internet of Things. Retrieved from https://wikibon.com/the-vital-role-of-edge-computing-in-the-internet-of-things.

[44]

The Linux Foundation. Accessed:. The Open Platform for the IoT Edge. Retrieved on July 25, 2021 from https://www.edgexfoundry.org.

[45]

Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth. 2019. A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*’19). Association for Computing Machinery, New York, NY, 329–338.

Digital Library

[46]

C. Gao, Antonio Rios-Navarro, Xi Chen, T. Delbrück, and Shih-Chii Liu. 2020. EdgeDRNN: Enabling low-latency recurrent neural network edge inference. In 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). 41–45.

[47]

Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. CoRR abs/1712.07557 (2017).

[48]

A. Ghoneim, G. Muhammad, S. U. Amin, and B. Gupta. 2018. Medical image forgery detection for smart healthcare. IEEE Commun. Mag. 56, 4 (Apr. 2018), 33–37.

[49]

Sridhar Gopinath, Nikhil Ghanathe, Vivek Seshadri, and Rahul Sharma. 2019. Compiling KB-sized machine learning models to tiny IoT devices. In 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’19). ACM, New York, NY, 79–95.

Digital Library

[50]

Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain. 2017. ProtoNN: Compressed and accurate kNN for resource-scarce devices. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 70. PMLR, 1331–1340. Retrieved from http://proceedings.mlr.press/v70/gupta17a.html.

Digital Library

[51]

Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep learning with limited numerical precision. In 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML’15). JMLR.org, 1737–1746. Retrieved from http://dl.acm.org/citation.cfm?id=3045118.3045303.

Digital Library

[52]

Ramyad Hadidi, Jiashen Cao, Michael S. Ryoo, and Hyesoon Kim. 2019. Robustly executing DNNs in IoT systems using coded distributed computing. In 56th Design Automation Conference 2019 (DAC’19). Association for Computing Machinery, New York, NY.

Digital Library

[53]

R. Hadidi, J. Cao, M. Woodward, M. S. Ryoo, and H. Kim. 2018. Distributed perception by collaborative robots. IEEE Robot. Autom. Lett. 3, 4 (2018), 3709–3716.

[54]

Song Han, Huizi Mao, and William J. Dally. 2015. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv:https://arxiv.org/abs/1510.00149.

[55]

Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, and Andrew Y. Ng. 2014. Deep Speech: Scaling up end-to-end speech recognition. arXiv:https://arxiv.org/abs/1412.5567.

[56]

Richard Harper. 2003. Inside the Smart House. Springer-Verlag, Berlin.

Digital Library

[57]

Evan Hennis, Mark Deoust, and Billy Lamberta. 2019. TensorFlow Lite Speech Command Recognition Android Demo. Retrieved from https://github.com/tensorflow/examples/tree/master/lite/examples/speech_commands/android.

[58]

Jacob Hochstetler, Rahul Padidela, Qing Chen, Qiang Yang, and Songnian Fu. 2018. Embedded deep learning for vehicular edge computing. In IEEE/ACM Symposium on Edge Computing (SEC). 341–343.

[59]

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:https://arxiv.org/abs/1704.04861.

[60]

C. C.-H. Hsu, M. Y.-C. Wang, H. C. H. Shen, R. H. Chiang, and C. H. P. Wen. 2017. FallCare+: An IoT surveillance system for fall detection. In International Conference on Applied System Innovation (ICASI). 921–922.

[61]

Z. Huai, B. Ding, H. Wang, M. Geng, and L. Zhang. 2019. Towards deep learning on resource-constrained robots: A crowdsourcing approach with model partition. In IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). 989–994.

[62]

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks. In 30th International Conference on Neural Information Processing Systems. 4114–4122.

Digital Library

[63]

Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv:https://arxiv.org/abs/1602.07360.

[64]

Michaela Iorga, Larry B. Feldman, Robert Barton, Michael Martin, Nedim S. Goren, and Charif Mahmoudi. 2018. Fog Computing Conceptual Model.

[65]

D. Isereau, C. Capraro, E. Cote, M. Barnell, and C. Raymond. 2017. Utilizing high-performance embedded computing, Agile Condor, for intelligent processing: An artificial intelligence platform for remotely piloted aircraft. In Intelligent Systems Conference (IntelliSys). 1155–1159.

[66]

Kaya Ismail. 2018. Edge Computing vs. Fog Computing: What’s the Difference?Retrieved from https://www.cmswire.com/information-management/edge-computing-vs-fog-computing-whats-the-difference/.

[67]

R. Colin Johnson. 2019. Neural Learning on the Edge. Retrieved from https://cacm.acm.org/news/234063-neural-learning-on-the-edge/fulltext.

[68]

Vinu Joseph, Ganesh L. Gopalakrishnan, Saurav Muralidharan, Michael Garland, and Animesh Garg. 2020. A programmable approach to neural network compression. IEEE Micro 40, 5 (Sep. 2020), 17–25.

Digital Library

[69]

Daniel Kang, Peter Bailis, and Matei Zaharia. 2019. Challenges and opportunities in DNN-based video analytics: A demonstration of the BlazeIt video query engine. In 9th Biennial Conference on Innovative Data Systems Research. Retrieved from http://cidrdb.org/cidr2019/papers/p141-kang-cidr19.pdf.

[70]

Duseok Kang, Euiseok Kim, Inpyo Bae, Bernhard Egger, and Soonhoi Ha. 2018. C-GOOD: C-code generation framework for optimized on-device deep learning. In International Conference on Computer-Aided Design (ICCAD’18). ACM, New York, NY.

Digital Library

[71]

Gorkem Kar, Shubham Jain, Marco Gruteser, Fan Bai, and Ramesh Govindan. 2017. Real-time traffic estimation at vehicular edge nodes. In 2nd ACM/IEEE Symposium on Edge Computing (SEC’17). ACM, New York, NY.

Digital Library

[72]

Jae-Yun Kim and Soo-Mook Moon. 2018. Blockchain-based edge computing for deep neural network applications. In Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA’18). Association for Computing Machinery, New York, NY, 53–55.

Digital Library

[73]

Ashish Kumar, Saurabh Goyal, and Manik Varma. 2017. Resource-efficient machine learning in 2 KB RAM for the internet of things. In 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 70. PMLR, 1935–1944. Retrieved from http://proceedings.mlr.press/v70/kumar17a.html.

Digital Library

[74]

Aditya Kusupati, Don Dennis, Chirag Gupta, Ashish Kumar, Shishir Patil, and Harsha Simhadri. 2021. The EdgeML Library: An ML library for machine learning on the Edge. Retrieved on July 25, 2021 from https://github.com/Microsoft/EdgeML.

[75]

Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, and Manik Varma. 2018. FastGRNN: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network. In Advances in Neural Information Processing Systems 31. Curran Associates, Inc., 9017–9028. Retrieved from http://papers.nips.cc/paper/8116-fastgrnn-a-fast-accurate-stable-and-tiny-kilobyte-sized-gated-recurrent-neural-network.pdf.

Digital Library

[76]

Gant Laborde. 2019. Perf Machine Learning on Rasp Pi. Retrieved from https://medium.com/free-code-camp/perf-machine-learning-on-rasp-pi-51101d03dba2.

[77]

Liangzhen Lai and Naveen Suda. 2018. Enabling deep learning at the IoT edge. In International Conference on Computer-Aided Design (ICCAD’18). ACM, New York, NY.

Digital Library

[78]

N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, L. Jiao, L. Qendro, and F. Kawsar. 2016. DeepX: A software accelerator for low-power deep learning inference on mobile devices. In 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 1–12.

Digital Library

[79]

S. Lee, K. Son, H. Kim, and J. Park. 2017. Car plate recognition based on CNN using embedded system with GPU. In 10th International Conference on Human System Interactions (HSI). 239–241.

[80]

D. Li, T. Salonidis, N. V. Desai, and M. C. Chuah. 2016. DeepCham: Collaborative edge-mediated adaptive deep learning for mobile object recognition. In IEEE/ACM Symposium on Edge Computing (SEC). 64–76.

[81]

En Li, Zhi Zhou, and Xu Chen. 2018. Edge intelligence: On-demand deep learning model co-inference with device-edge synergy. In Workshop on Mobile Edge Communications (MECOMM’18). ACM, New York, NY, 31–36.

Digital Library

[82]

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, and Depei Qian. 2020. The Deep Learning Compiler: A Comprehensive Survey. arXiv:https://arxiv.org/abs/2002.03794.

[83]

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2019. Federated learning: Challenges, methods, and future directions. Arxiv Eprints abs/1908.07873 (2019).

[84]

Le Liang, Hao Ye, and Geoffrey Ye Li. 2018. Toward intelligent vehicular networks: A machine learning framework. IEEE Internet Things J. 6, 1 (2018), 124–135.

[85]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao. 2020. Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 22, 3 (2020), 2031–2063.

[86]

Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2020. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. arXiv:https://arxiv.org/abs/1712.01887.

[87]

Zhong Qiu Lin, Audrey G. Chung, and Alexander Wong. 2018. EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge. arXiv:https://arxiv.org/abs/1810.08559.

[88]

C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane, M. Yunsheng, S. Chen, and P. Hou. 2018. A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Trans. Serv.Comput. 11, 2 (Mar. 2018), 249–261.

[89]

Lumin Liu, Jun Zhang, S. H. Song, and Khaled B. Letaief. 2019. Client-Edge-Cloud Hierarchical Federated Learning. arXiv:https://arxiv.org/abs/1905.06641

[90]

L. Liu, X. Zhang, M. Qiao, and W. Shi. 2018. SafeShareRide: Edge-based attack detection in ridesharing services. In IEEE/ACM Symposium on Edge Computing (SEC). 17–29.

[91]

Qiang Liu, Siqi Huang, and Tao Han. 2017. Fast and accurate object analysis at the edge for mobile augmented reality: Demo. In 2nd ACM/IEEE Symposium on Edge Computing (SEC’17). ACM, New York, NY.

Digital Library

[92]

Wang Luping, Wang Wei, and Li Bo. 2019. CMFL: Mitigating communication overhead for federated learning. https://doi.org/10.1109/ICDCS.2019.00099

[93]

Salma Abdel Magid, Francesco Petrini, and Behnam Dezfouli. 2020. Image classification on IoT edge devices: Profiling and modeling. Clust. Comput. 23, 2 (2020), 1025–1043.

Digital Library

[94]

Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, and Amit P. Sheth. 2018. Machine learning for internet of things data analysis: A survey. Dig. Commun. Netw. 4, 3 (2018), 161–175.

[95]

James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, Jacques Bughin, and Dan Aharon. 2015. The Internet of Things: Mapping the Value Behind the Hype. Technical Report. McKinsey and Company.

[96]

J. Mao, X. Chen, K. W. Nixon, C. Krieger, and Y. Chen. 2017. MoDNN: Local distributed mobile computing system for deep neural network. In Design, Automation Test in Europe Conference Exhibition (DATE). 1396–1401.

Digital Library

[97]

Y. Mao, S. Yi, Q. Li, J. Feng, F. Xu, and S. Zhong. 2018. Learning from differentially private neural activations with edge computing. In IEEE/ACM Symposium on Edge Computing (SEC). 90–102.

[98]

C. Marantos, N. Karavalakis, V. Leon, V. Tsoutsouras, K. Pekmestzi, and D. Soudris. 2018. Efficient support vector machines implementation on Intel/Movidius Myriad 2. In 7th International Conference on Modern Circuits and Systems Technologies (MOCAST). 1–4.

[99]

V. Mazzia, A. Khaliq, F. Salvetti, and M. Chiaberge. 2020. Real-time apple detection system using embedded systems with hardware accelerators: An edge AI application. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 954–964.

[100]

Bradley McDanel, Surat Teerapittayanon, and H. T. Kung. 2017. Embedded Binarized Neural Networks. arXiv:https://arxiv.org/abs/1709.02260.

[101]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), Vol. 54. PMLR, 1273–1282. Retrieved from http://proceedings.mlr.press/v54/mcmahan17a.html.

[102]

Microsoft. 2018. Embedded Learning Library. https://microsoft.github.io/ELL/.

[103]

Microsoft. 2019. Project Brainwave. Retrieved from https://www.microsoft.com/en-us/research/project/project-brainwave/.

[104]

S. A. Miraftabzadeh, P. Rad, K. R. Choo, and M. Jamshidi. 2018. A privacy-aware architecture at the edge for autonomous real-time identity reidentification in crowds. IEEE Internet Things J. 5, 4 (Aug. 2018), 2936–2946.

[105]

M. G. S. Murshed, J. J. Carroll, N. Khan, and F. Hussain. 2020. Resource-aware on-device deep learning for supermarket hazard detection. In 19th IEEE International Conference on Machine Learning and Applications (ICMLA). 871–876.

[106]

Pedro Navarro Lorente, Carlos Fernandez, Raul Borraz, and Diego Alonso. 2016. A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3D range data. Sensors 17 (12 2016), 18.

[107]

Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y. Ng. 2011. Multimodal deep learning. In 28th International Conference on International Conference on Machine Learning (ICML’11). Omnipress, 689–696. Retrieved from http://dl.acm.org/citation.cfm?id=3104482.3104569.

Digital Library

[108]

Seyed Yahya Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, and Timothy R. Faughnan. 2018. Intelligent surveillance as an edge network service: from Harr-Cascade, SVM to a lightweight CNN. arxiv:1805.00331.

[109]

Takayuki Nishio and Ryo Yonetani. 2018. Client selection for federated learning with heterogeneous resources in mobile edge. In IEEE International Conference on Communications (ICC). 1–7.

[110]

Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-garadi, and Uzoma Rita Alo. 2018. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Exp. Syst. Applic. 105 (2018), 233–261.

[111]

Samuel S. Ogden and Tian Guo. 2018. MODI: Mobile deep inference made efficient by edge computing. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge’18). USENIX Association. Retrieved from https://www.usenix.org/conference/hotedge18/presentation/ogden.

[112]

S. A. Osia, A. S. Shamsabadi, A. Taheri, H. R. Rabiee, and H. Haddadi. 2018. Private and scalable personal data analytics using hybrid edge-to-cloud deep learning. Computer 51, 5 (May 2018), 42–49.

[113]

Anand Oswal. 2018. Time to Get Serious About Edge Computing. Retrieved from https://blogs.cisco.com/enterprise/time-to-get-serious-about-edge-computing.

[114]

Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W. Keckler, and William J. Dally. 2017. SCNN: An accelerator for compressed-sparse convolutional neural networks. SIGARCH Comput. Archit. News 45, 2 (June 2017), 27–40.

Digital Library

[115]

Donghyun Park, Seulgi Kim, Yelin An, and Jae-Yoon Jung. 2018. LiReD: A light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks. Sensors 18, 7 (2018).

[116]

Eunhyeok Park, Dongyoung Kim, and Sungjoo Yoo. 2018. Energy-efficient neural network accelerator based on outlier-aware low-precision computation. In 45th International Symposium on Computer Architecture (ISCA’18). IEEE Press, 688–698.

Digital Library

[117]

J. Park, S. Samarakoon, M. Bennis, and M. Debbah. 2019. Wireless network intelligence at the edge. Proc. IEEE 107, 11 (2019), 2204–2239.

[118]

David Patterson and Andrew Waterman. 2017. The RISC-V Reader: An Open Architecture Atlas. Strawberry Canyon LLC.

Digital Library

[119]

Diego Peteiro-Barral and Bertha Guijarro-Berdiñas. 2013. A survey of methods for distributed machine learning. Prog. Artif. Intell. 2, 1 (01 Mar. 2013), 1–11.

[120]

K. Pradeep, K. Kamalavasan, R. Natheesan, and A. Pasqual. 2018. EdgeNet: SqueezeNet like convolution neural network on embedded FPGA. In 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). 81–84.

[121]

MD Abdur Rahman, M. Shamim Hossain, George Loukas, Elham Hassanain, Syed Sadiqur Rahman, Mohammed F. Alhamid, and Mohsen Guizani. 2018. Blockchain-based mobile edge computing framework for secure therapy applications. IEEE Access 6 (2018), 72469–72478.

[122]

Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. arXiv:https://arxiv.org/abs/1804.02767.

[123]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should I trust you?”: Explaining the predictions of any classifier. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). Association for Computing Machinery, New York, NY, 1135–1144.

Digital Library

[124]

Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Garret Catron, Summer Deng, Roman Dzhabarov, Nick Gibson, James Hegeman, Meghan Lele, Roman Levenstein, Jack Montgomery, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Misha Smelyanskiy, and Man Wang. 2019. Glow: Graph Lowering Compiler Techniques for Neural Networks. arXiv:https://arxiv.org/abs/1805.00907.

[125]

K. Rungsuptaweekoon, V. Visoottiviseth, and R. Takano. 2017. Evaluating the power efficiency of deep learning inference on embedded GPU systems. In 2nd International Conference on Information Technology (INCIT). 1–5.

[126]

M. Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1 (Jan. 2017), 30–39.

Digital Library

[127]

M. Satyanarayanan and N. Davies. 2019. Augmenting cognition through edge computing. Computer 52, 7 (July 2019), 37–46.

[128]

Ragini Sharma, Saman Biookaghazadeh, and Ming Zhao. 2018. Are existing knowledge transfer techniques effective for deep learning on edge devices? In 27th International Symposium on High-performance Parallel and Distributed Computing (HPDC’18). ACM, New York, NY, 15–16.

Digital Library

[129]

Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE Internet Things J. 3 (2016), 637–646.

[130]

W. Shi and S. Dustdar. 2016. The promise of edge computing. Computer 49, 5 (May 2016), 78–81.

Digital Library

[131]

Z. Song, B. Fu, F. Wu, Z. Jiang, L. Jiang, N. Jing, and X. Liang. 2020. DRQ: Dynamic region-based quantization for deep neural network acceleration. In ACM/IEEE 47th International Symposium on Computer Architecture (ISCA). 1010–1021.

Digital Library

[132]

Flávio Souza, Diego de Las Casas, Vinícius Flores, SunBum Youn, Meeyoung Cha, Daniele Quercia, and Virgílio Almeida. 2015. Dawn of the Selfie Era: The Whos, Wheres, and Hows of Selfies on Instagram. arXiv:https://arxiv.org/abs/1510.05700.

Digital Library

[133]

IBM Research Editorial Staff. 2017. IBM scientists team with The Weather Company to bring edge computing to life. Retrieved from https://www.ibm.com/blogs/research/2017/02/bringing-edge-computing-to-life/.

[134]

Rafael Stahl, Zhuoran Zhao, Daniel Mueller-Gritschneder, Andreas Gerstlauer, and Ulf Schlichtmann. 2019. Fully distributed deep learning inference on resource-constrained edge devices. In Embedded Computer Systems: Architectures, Modeling, and Simulation, Dionisios N. Pnevmatikatos, Maxime Pelcat, and Matthias Jung (Eds.). Springer International Publishing, Cham, 77–90.

[135]

Mingxing Tan and Quoc V. Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. Arxiv Eprints 1905.11946 (2019).

[136]

B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, and Q. Yang. 2017. Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Industr. Inform. 13, 5 (Oct. 2017), 2140–2150.

[137]

Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, and Sagar Jain. 2020. Understanding and improving knowledge distillation. Arxiv Eprints 2002.03532 (2020).

[138]

Zeyi Tao and Qun Li. 2018. eSGD: Communication efficient distributed deep learning on the edge. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge’18). USENIX Association. https://www.usenix.org/conference/hotedge18/presentation/tao.

[139]

GreenWaves Technologies. 2018. GAP8 - GreenWaves. Retrieved from https://en.wikichip.org/wiki/greenwaves/gap8.

[140]

GreenWaves Technologies. 2019. TF2GAP8. Retrieved from https://github.com/GreenWaves-Technologies/tf2gap8.

[141]

S. Teerapittayanon, B. McDanel, and H. T. Kung. 2017. Distributed deep neural networks over the cloud, the edge and end devices. In IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 328–339.

[142]

Tom Simonite. 2019. The best algorithms struggle to recognize black faces equally. Retrieved from https://www.wired.com/story/best-algorithms-struggle-recognize-% black-faces-equally/.

[143]

Shreshth Tuli, Nipam Basumatary, and Rajkumar Buyya. 2019. EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments. arXiv:https://arxiv.org/abs/1906.11056.

[144]

S. Ullah and D. Kim. 2020. Benchmarking Jetson platform for 3D point-cloud and hyper-spectral image classification. In IEEE International Conference on Big Data and Smart Computing (BigComp). 477–482.

[145]

Sahar Voghoei, Navid Hashemi Tonekaboni, Jason G. Wallace, and Hamid Reza Arabnia. 2018. Deep learning at the edge. In International Conference on Computational Science and Computational Intelligence (CSCI). 895–901.

[146]

C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou. 2017. DLAU: A scalable deep learning accelerator unit on FPGA. IEEE Trans. Comput.-aided Des. Integ. Circ. Syst. 36, 3 (Mar. 2017), 513–517.

Digital Library

[147]

J. Wang, Z. Feng, Z. Chen, S. George, M. Bala, P. Pillai, S. Yang, and M. Satyanarayanan. 2018. Bandwidth-efficient live video analytics for drones via edge computing. In IEEE/ACM Symposium on Edge Computing (SEC). 159–173.

[148]

Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin S. Chan. 2018. When edge meets learning: Adaptive control for resource-constrained distributed machine learning. In IEEE Conference on Computer Communications. 63–71.

[149]

Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, and Xu Chen. 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Commun. Surv. Tutor. 22, 2 (2020), 869–904.

[150]

Sally Ward-Foxton. 2019. AI at the Very, Very Edge. Retrieved from https://www.eetimes.com/document.asp?doc_id=1334918.

[151]

Pete Warden. 2018. Speech Commands: A Dataset for Limited-vocabulary Speech Recognition. arXiv:https://arxiv.org/abs/1804.03209.

[152]

Matt Welsh. 2019. True AI on a Raspberry Pi, with no extra hardware. Retrieved from https://medium.com/@mdwdotla/true-ai-on-a-raspberry-pi-with-no-extra-hardware-dcdbff12d068.

[153]

R. Xu, S. Y. Nikouei, Y. Chen, A. Polunchenko, S. Song, C. Deng, and T. R. Faughnan. 2018. Real-time human objects tracking for smart surveillance at the edge. In IEEE International Conference on Communications (ICC). 1–6.

[154]

Zhuangdi Xu, Harshit Gupta, and Umakishore Ramachandran. 2018. STTR: A system for tracking all vehicles all the time at the edge of the network. In 12th ACM International Conference on Distributed and Event-based Systems (DEBS’18). ACM, New York, NY, 124–135.

Digital Library

[155]

Tzu-Hsien Yang, Hsiang-Yun Cheng, Chia-Lin Yang, I-Ching Tseng, Han-Wen Hu, Hung-Sheng Chang, and Hsiang-Pang Li. 2019. Sparse ReRAM engine: Joint exploration of activation and weight sparsity in compressed neural networks. In 46th International Symposium on Computer Architecture (ISCA’19). Association for Computing Machinery, New York, NY, 236–249.

Digital Library

[156]

Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, and Tarek F. Abdelzaher. 2017. Compressing deep neural network structures for sensing systems with a compressor-critic framework. arxiv:1706.01215.

[157]

Mahmut Taha Yazici, Shadi Basurra, and Mohamed Medhat Gaber. 2018. Edge machine learning: Enabling smart internet of things applications. Big Data Cog. Comput. 2, 3 (2018), 26.

[158]

Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, and Scott Mahlke. 2017. Scalpel: Customizing DNN pruning to the underlying hardware parallelism. In 44th International Symposium on Computer Architecture (ISCA’17). Association for Computing Machinery, New York, NY, 548–560.

Digital Library

[159]

Qunsong Zeng, Yuqing Du, Kin K. Leung, and Kaibin Huang. 2019. Energy-efficient Radio Resource Allocation for Federated Edge Learning. arXiv:https://arxiv.org/abs/1907.06040.

[160]

Jiaqi Zhang, Xiangru Chen, Mingcong Song, and Tao Li. 2019. Eager pruning: Algorithm and architecture support for fast training of deep neural networks. In 46th International Symposium on Computer Architecture (ISCA’19). Association for Computing Machinery, New York, NY, 292–303.

Digital Library

[161]

Jianhao Zhang, Yingwei Pan, Ting Yao, He Zhao, and Tao Mei. 2019. daBNN: A super fast inference framework for binary neural networks on arm devices. In 27th ACM International Conference on Multimedia. 2272–2275.

Digital Library

[162]

Xingzhou Zhang, Yifan Wang, and Weisong Shi. 2018. pCAMP: Performance comparison of machine learning packages on the edges. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge’18). USENIX Association, Boston, MA. Retrieved from https://www.usenix.org/conference/hotedge18/presentation/zhang.

[163]

X. Zhang, X. Zhou, M. Lin, and J. Sun. 2018. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6848–6856.

[164]

Zhuoran Zhao, Kamyar Mirzazad Barijough, and Andreas Gerstlauer. 2018. DeepThings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans. Comput.-aided Des. Integ. Circ. Syst. PP (10 2018), 1–1.

[165]

Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang. 2019. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 8 (Aug. 2019), 1738–1762.

[166]

Guangxu Zhu, Dongzhu Liu, Yuqing Du, Changsheng You, Jun Zhang, and Kaibin Huang. 2018. Towards an Intelligent Edge: Wireless Communication Meets Machine Learning. arXiv:https://arxiv.org/abs/1809.00343.

Cited By

Hohman FWang CLee JGörtler JMoritz DBigham JRen ZForet CShan QZhang X(2024)Talaria: Interactively Optimizing Machine Learning Models for Efficient InferenceProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642628(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642628
Hohman FKery MRen DMoritz D(2024)Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning ExperiencesProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642109(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642109
Andrade PSilva IDiniz MFlores TCosta DSoares E(2024)Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression TechniqueACM Transactions on Embedded Computing Systems10.1145/359135623:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3591356
Show More Cited By

Index Terms

Machine Learning at the Network Edge: A Survey

Recommendations

A top-down survey on securing IoT with machine learning: goals, recent advances and challenges

The Internet of Things (IoT) has seen it all from being just another innovation to a leading technology; it is now a binding force that interconnects various aspects of our lives. The IoT's tremendous growth is driven by emerging applications and evolving ...
Read More
Machine learning-based computation offloading in edge and fog: a systematic review
Abstract
Today, Mobile Cloud Computing (MCC) alone can no longer respond to the increasing volume of data and satisfy the necessary delays in real-time applications. In addition, challenges such as security, energy consumption, storage space, bandwidth, ...
Read More
Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective
Algorithms and Architectures for Parallel Processing
Abstract
Federated learning is considered to be a privacy-preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 8

November 2022

754 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3481697

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2021

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 October 2020

Published in CSUR Volume 54, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

82
Total Citations
View Citations
4,221
Total Downloads

Downloads (Last 12 months)1,379
Downloads (Last 6 weeks)134

Other Metrics

View Author Metrics

Citations

Cited By

Hohman FWang CLee JGörtler JMoritz DBigham JRen ZForet CShan QZhang X(2024)Talaria: Interactively Optimizing Machine Learning Models for Efficient InferenceProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642628(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642628
Hohman FKery MRen DMoritz D(2024)Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning ExperiencesProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642109(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642109
Andrade PSilva IDiniz MFlores TCosta DSoares E(2024)Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression TechniqueACM Transactions on Embedded Computing Systems10.1145/359135623:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3591356
Sirkiä JPanula TKaisti M(2024)Wearable edge machine learning with synthetic photoplethysmogramsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121523238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121523
Hara TSasabe M(2024)Practicality of in-kernel/user-space packet processing empowered by lightweight neural network and decision treeComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110188240:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110188
Szabó GPető J(2024)Intelligent wireless resource management in industrial camera systemsComputer Communications10.1016/j.comcom.2023.12.026216:C(68-85)Online publication date: 15-Feb-2024
https://dl.acm.org/doi/10.1016/j.comcom.2023.12.026
Rodriguez-Conde ICampos CFdez-Riverola F(2023)Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoTSensors10.3390/s2304191123:4(1911)Online publication date: 8-Feb-2023
https://doi.org/10.3390/s23041911
BÜYÜKTANIR BYILDIZ KÜLKÜ EBÜYÜKTANIR T(2023)du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisiGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.108774638:3(1919-1930)Online publication date: 6-Jan-2023
https://doi.org/10.17341/gazimmfd.1087746
Peltonen EDias S(2023)LinkEdge: Open-sourced MLOps Integration with IoT EdgeProceedings of the 3rd Eclipse Security, AI, Architecture and Modelling Conference on Cloud to Edge Continuum10.1145/3624486.3624496(67-76)Online publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1145/3624486.3624496
Magalhães JWoodruff JPolgreen EO'Boyle MDe Roover CRumpe BShaikhha A(2023)C2TACO: Lifting Tensor Code to TACOProceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3624007.3624053(42-56)Online publication date: 22-Oct-2023
https://dl.acm.org/doi/10.1145/3624007.3624053
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents