research-article

Jointly optimizing preprocessing and inference for DNN-based visual analytics

Authors:

Teja Veeramacheneni,

Matei ZahariaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 2

Pages 87 - 100

https://doi.org/10.14778/3425879.3425881

Published: 01 October 2020 Publication History

Abstract

While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researchers have proposed systems and optimizations to reduce these costs by allowing users to trade off accuracy and speed. In this work, we examine end-to-end DNN execution in visual analytics systems on modern accelerators. Through a novel measurement study, we show that the preprocessing of data (e.g., decoding, resizing) can be the bottleneck in many visual analytics systems on modern hardware.

To address the bottleneck of preprocessing, we introduce two optimizations for end-to-end visual analytics systems. First, we introduce novel methods of achieving accuracy and throughput trade-offs by using natively present, low-resolution visual data. Second, we develop a runtime engine for efficient visual DNN inference. This runtime engine a) efficiently pipelines preprocessing and DNN execution for inference, b) places preprocessing operations on the CPU or GPU in a hardware- and input-aware manner, and c) efficiently manages memory and threading for high throughput execution. We implement these optimizations in a novel system, Smol, and evaluate Smol on eight visual datasets. We show that its optimizations can achieve up to 5.9X end-to-end throughput improvements at a fixed accuracy over recent work in visual analytics.

References

[1]

2018. MLPerf. https://mlperf.org/.

[2]

2019. NVIDIA TensorRT. https://developer.nvidia.com/tensorrt

[3]

Jorge Albericio, Alberto Delmás, Patrick Judd, Sayeh Sharify, Gerard O'Leary, Roman Genov, and Andreas Moshovos. 2017. Bit-pragmatic deep neural network computing. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, 382--394.

Digital Library

[4]

Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, and Andreas Moshovos. 2016. Cnvlutin: Ineffectual-neuron-free deep neural network computing. In ACM SIGARCH Computer Architecture News, Vol. 44. IEEE Press, 1--13.

Digital Library

[5]

Corrado Alessio. 2019. Animals-10. https://www.kaggle.com/alessiocorrado99/animals10

[6]

Michael R Anderson, Michael Cafarella, Thomas F Wenisch, and German Ros. 2019. Predicate Optimization for a Visual Analytics Database. ICDE (2019).

[7]

Elizabeth Arens. 2019. Always Up-to-Date Guide to Social Media Image Sizes. https://sproutsocial.com/insights/social-media-image-sizes-guide/

[8]

Christopher M Bishop. 2006. Pattern recognition and machine learning. springer.

Digital Library

[9]

Tom B Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, and Ian Goodfellow. 2018. Unrestricted adversarial examples. arXiv preprint arXiv:1809.08352 (2018).

[10]

Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David Andersen, Michael Kaminsky, and Subramanya Dulloor. 2019. Scaling Video Analytics on Constrained Edge Nodes. SysML (2019).

[11]

Srimat Chakradhar, Murugan Sankaradas, Venkata Jakkula, and Srihari Cadambi. 2010. A dynamically configurable coprocessor for convolutional neural networks. ACM SIGARCH Computer Architecture News 38, 3 (2010), 247--257.

Digital Library

[12]

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018. {TVM}: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18). 578--594.

Digital Library

[13]

Yunji Chen, Tianshi Chen, Zhiwei Xu, Ninghui Sun, and Olivier Temam. 2016. DianNao family: energy-efficient hardware accelerators for machine learning. Commun. ACM 59, 11 (2016), 105--112.

Digital Library

[14]

Yu-Hsin Chen, Joel Emer, and Vivienne Sze. 2016. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks. In ACM SIGARCH Computer Architecture News, Vol. 44. IEEE Press, 367--379.

Digital Library

[15]

Ping Chi, Shuangchen Li, Cong Xu, Tao Zhang, Jishen Zhao, Yongpan Liu, Yu Wang, and Yuan Xie. 2016. Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory. In ACM SIGARCH Computer Architecture News, Vol. 44. IEEE Press, 27--39.

Digital Library

[16]

François Chollet et al. 2015. Keras.

[17]

Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Re, and Matei Zaharia. 2018. Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. arXiv preprint arXiv:1806.01427 (2018).

[18]

Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia. 2017. DAWNBench: An End-to-End Deep Learning Benchmark and Competition. Training 100, 101 (2017), 102.

[19]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.

[20]

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, and Trevor Gale. 2016. dMath: Distributed Linear Algebra for DL. arXiv preprint arXiv:1611.07819 (2016).

[21]

Clément Farabet, Berin Martini, Benoit Corda, Polina Akselrod, Eugenio Culurciello, and Yann LeCun. 2011. Neuflow: A runtime reconfigurable dataflow processor for vision. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on. IEEE, 109--116.

[22]

Jeremy Fowers, Kalin Ovtcharov, Michael Papamichael, Todd Massengill, Ming Liu, Daniel Lo, Shlomi Alkalay, Michael Haselman, Logan Adams, Mahdi Ghandi, et al. 2018. A configurable cloud-scale DNN processor for real-time AI. In Proceedings of the 45th Annual International Symposium on Computer Architecture. IEEE Press, 1--14.

Digital Library

[23]

T Gale, S Eliuk, and C Upright. 2017. High-Performance Data Loading and Augmentation for Deep Neural Network Training. In GPU technology conference 2017.

[24]

Vinayak Gokhale, Jonghoon Jin, Aysegul Dundar, Berin Martini, and Eugenio Culurciello. 2014. A240 g-ops/s mobile coprocessor for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 682--687.

Digital Library

[25]

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A Horowitz, and William J Dally. 2016. EIE: efficient inference engine on compressed deep neural network. In Computer Architecture (ISCA), 2016 ACM/IEEE 43rd Annual International Symposium on. IEEE, 243--254.

Digital Library

[26]

Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).

[27]

Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In ICCV. IEEE, 2980--2988.

[28]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.

[29]

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).

[30]

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 preprint arXiv:1704.04861 (2017).

[31]

Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Paramvir Bahl, Matthai Philipose, Phillip B Gibbons, and Onur Mutlu. 2018. Focus: Querying Large Video Datasets with Low Latency and Low Cost. OSDI (2018).

Digital Library

[32]

Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al. 2017. In-datacenter performance analysis of a tensor processing unit. In Computer Architecture (ISCA), 2017 ACM/IEEE 44th Annual International Symposium on. IEEE, 1--12.

Digital Library

[33]

Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor M Aamodt, and Andreas Moshovos. 2016. Stripes: Bit-serial deep neural network computing. In Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on. IEEE, 1--12.

[34]

Daniel Kang, Peter Bailis, and Matei Zaharia. 2019. BlazeIt: optimizing declarative aggregation and limit queries for neural network-based video analytics. Proceedings of the VLDB Endowment 13, 4 (2019), 533--546.

Digital Library

[35]

Daniel Kang, Peter Bailis, and Matei Zaharia. 2019. Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. CIDR.

[36]

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. 2017. NoScope: optimizing neural network queries over video at scale. PVLDB 10, 11 (2017), 1586--1597.

Digital Library

[37]

Daniel Kang, Edward Gan, Peter Bailis, Tatsunori Hashimoto, and Matei Zaharia. 2020. Approximate Selection with Guarantees using Proxies. PVLDB (2020).

Digital Library

[38]

Daniel Kang, Ankit Mathur, Teja Veeramacheneni, Peter Bailis, and Matei Zaharia. 2020. Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. arXiv preprint arXiv:2007.13005 (2020).

[39]

Chris Leary and Todd Wang. 2017. XLA: TensorFlow, compiled. TensorFlow Dev Summit (2017).

[40]

Shuangchen Li, Dimin Niu, Krishna T Malladi, Hongzhong Zheng, Bob Brennan, and Yuan Xie. 2017. Drisa: A dram-based reconfigurable in-situ accelerator. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, 288--301.

Digital Library

[41]

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. Ssd: Single shot multibox detector. In European conference on computer vision. Springer, 21--37.

[42]

Yao Lu, Aakanksha Chowdhery, Srikanth Kandula, and Surajit Chaudhuri. 2018. Accelerating Machine Learning Inference with Probabilistic Predicates. In SIGMOD. ACM, 1493--1508.

Digital Library

[43]

Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, et al. 2019. Mlperf training benchmark. arXiv preprint arXiv:1910.01500 (2019).

[44]

Bert Moons and Marian Verhelst. 2016. A 0.3--2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets. In VLSI Circuits (VLSI-Circuits), 2016 IEEE Symposium on. IEEE, 1--2.

[45]

NVIDIA. 2019. NVIDIA DALI. https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/index.html

[46]

NVIDIA. 2020. NVIDIA T4 Tensor Core GPU for AI Inference. https://www.nvidia.com/en-us/data-center/tesla-t4/

[47]

Shoumik Palkar, James J Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Matei Zaharia, and Stanford InfoLab. 2017. Weld: A common runtime for high performance data analytics. In Conference on Innovative Data Systems Research (CIDR).

[48]

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. In ACM SIGARCH Computer Architecture News, Vol. 45. ACM, 27--40.

Digital Library

[49]

Seong-Wook Park, Junyoung Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, and Hoi-Jun Yoo. 2015. An energy-efficient and scalable deep learning/inference processor with tetra-parallel MIMD architecture for big data applications. IEEE transactions on biomedical circuits and systems 9, 6 (2015), 838--848.

[50]

Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017).

[51]

Maurice Peemen, Arnaud AA Setio, Bart Mesman, Henk Corporaal, et al. 2013. Memory-centric accelerator design for Convolutional Neural Networks. In ICCD, Vol. 2013. 13--19.

[52]

William B Pennebaker and Joan L Mitchell. 1992. JPEG: Still image data compression standard. Springer Science & Business Media.

Digital Library

[53]

Alex Poms, William Crichton, Pat Hanrahan, and Kayvon Fatahalian. 2018. Scanner: Efficient Video Analysis at Scale (To Appear). (2018).

Digital Library

[54]

PyTorch Team. 2018. The road to 1.0: production ready PyTorch. https://pytorch.org/blog/the-road-to-1_0/

[55]

Atul Rahman, Jongeun Lee, and Kiyoung Choi. 2016. Efficient FPGA acceleration of convolutional neural networks using logical-3D compute array. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2016. IEEE, 1393--1398.

Digital Library

[56]

Brandon Reagen, Paul Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, and David Brooks. 2016. Minerva: Enabling low-power, highly-accurate deep neural network accelerators. In ACM SIGARCH Computer Architecture News, Vol. 44. IEEE Press, 267--278.

Digital Library

[57]

Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, et al. 2019. Mlperf inference benchmark. arXiv preprint arXiv:1911.02549 (2019).

[58]

Daniel Richins, Dharmisha Doshi, Matthew Blackmore, Aswathy Thulaseedharan Nair, Neha Pathapati, Ankit Patel, Brainard Daguman, Daniel Dobrijalowski, Ramesh Illikkal, Kevin Long, et al. 2020. Missing the Forest for the Trees: End-to-End AI Application Performance in Edge Data Centers. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 515--528.

[59]

Yongming Shen, Michael Ferdman, and Peter Milder. 2017. Maximizing CNN accelerator efficiency through resource partitioning. In Computer Architecture (ISCA), 2017 ACM/IEEE 44th Annual International Symposium on. IEEE, 535--547.

Digital Library

[60]

Gary J Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand. 2012. Overview of the high efficiency video coding (HEVC) standard. IEEE Transactions on circuits and systems for video technology 22, 12 (2012), 1649--1668.

Digital Library

[61]

Mingxing Tan and Quoc V Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019).

[62]

Mingxing Tan, Ruoming Pang, and Quoc V Le. 2019. Efficientdet: Scalable and efficient object detection. arXiv preprint arXiv:1911.09070 (2019).

[63]

David Taubman and Michael Marcellin. 2012. JPEG2000 image compression fundamentals, standards and practice: image compression fundamentals, standards and practice. Vol. 642. Springer Science & Business Media.

Digital Library

[64]

Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, et al. 2017. Scaledeep: A scalable compute architecture for learning and evaluating deep networks. In ACM SIGARCH Computer Architecture News, Vol. 45. ACM, 13--26.

Digital Library

[65]

Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The caltech-ucsd birds-200-2011 dataset. (2011).

[66]

Gregory K Wallace. 1992. The JPEG still picture compression standard. IEEE transactions on consumer electronics 38, 1 (1992), xviii--xxxiv.

Digital Library

[67]

Thomas Wiegand, Gary J Sullivan, Gisle Bjontegaard, and Ajay Luthra. 2003. Overview of the H. 264/AVC video coding standard. IEEE Transactions on circuits and systems for video technology 13, 7 (2003), 560--576.

Digital Library

[68]

Hao Wu. 2019. Low Precision Inference on GPU. https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9659-inference-at-reduced-precision-on-gpus.pdf

[69]

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1492--1500.

[70]

Tiantu Xu, Luis Materon Botelho, and Felix Xiaozhu Lin. 2019. VStore: A Data Store for Analytics on Large Videos. In Proceedings of the Fourteenth EuroSys Conference 2019. ACM, 16.

Digital Library

[71]

Haoyu Zhang, Ganesh Ananthanarayanan, Peter Bodik, Matthai Philipose, Paramvir Bahl, and Michael J Freedman. 2017. Live Video Analytics at Scale with Approximation and Delay-Tolerance. In NSDI, Vol. 9. 1.

Digital Library

[72]

Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, and Yichen Wei. 2017. Flow-guided feature aggregation for video object detection. arXiv preprint arXiv:1703.10025 (2017).

Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639307
Xu YZhang DZhang SWu SFeng ZChen G(2024)Predictive and Near-Optimal Sampling for View Materialization in Video DatabasesProceedings of the ACM on Management of Data10.1145/36392742:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639274
Wang ZWang YDeng JZheng DLi ADing YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input PreprocessingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640406(964-979)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640406
Show More Cited By

Recommendations

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures
MLHPC'17: Proceedings of the Machine Learning on HPC Environments

Traditionally, Deep Learning (DL) frameworks like Caffe, TensorFlow, and Cognitive Toolkit exploited GPUs to accelerate the training process. This has been primarily achieved by aggressive improvements in parallel hardware as well as through ...
Optimizing N-dimensional, winograd-based convolution for manycore CPUs
PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Recent work on Winograd-based convolution allows for a great reduction of computational complexity, but existing implementations are limited to 2D data and a single kernel size of 3 by 3. They can achieve only slightly better, and often worse performance ...
Optimizing the bayesian inference of phylogeny on graphic processors
CCGRID '15: Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing

Searching for the evolutionary relationships between groups of organism has become a routine procedure in molecular biology. MrBayes is a popular model based phylogenetic inference tool using Bayesian statistics. Unfortunately, the computational cost is ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 2

October 2020

167 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 October 2020

Published in PVLDB Volume 14, Issue 2

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
151
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sirin UIdreos S(2024)The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage FormatProceedings of the ACM on Management of Data10.1145/36393072:1(1-31)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639307
Xu YZhang DZhang SWu SFeng ZChen G(2024)Predictive and Near-Optimal Sampling for View Materialization in Video DatabasesProceedings of the ACM on Management of Data10.1145/36392742:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639274
Wang ZWang YDeng JZheng DLi ADing YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input PreprocessingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640406(964-979)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640406
Kang DGuibas JBailis PHashimoto TSun YZaharia M(2024)Data Management for ML-Based Analytics and BeyondACM / IMS Journal of Data Science10.1145/36110931:1(1-23)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3611093
Russo MHashimoto TKang DSun YZaharia M(2023)Accelerating Aggregation Queries on Unstructured Streams of DataProceedings of the VLDB Endowment10.14778/3611479.361149616:11(2897-2910)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611496
Nan YJiang SLi M(2023)Large-scale Video Analytics with Cloud–Edge Collaborative Continuous LearningACM Transactions on Sensor Networks10.1145/362447820:1(1-23)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3624478
Yuan MZhang LYou XLi XSchulzrinne HKohler EMaltz DMisra V(2023)PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at ScaleProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604825(724-737)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3604825
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3323091
Isenko AMayer RJedele JJacobsen HIves ZBonifati AEl Abbadi A(2022)Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing PipelinesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517848(1825-1839)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517848
Murray DŠimša JKlimovic AIndyk I(2021)tf.dataProceedings of the VLDB Endowment10.14778/3476311.347637414:12(2945-2958)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476311.3476374
Show More Cited By

View Options

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.

Media

Figures

Other

Tables

View Issue’s Table of Contents