research-article

DeepCache: Principled Cache for Mobile Deep Vision

Authors:

Felix Xiaozhu Lin,

Xuanzhe LiuAuthors Info & Claims

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

Pages 129 - 144

https://doi.org/10.1145/3241539.3241563

Published: 15 October 2018 Publication History

Abstract

We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. It addresses a key challenge raised by mobile vision: the cache must operate under video scene variation, while trading off among cacheability, overhead, and loss in model accuracy. At the input of a model, DeepCache discovers video temporal locality by exploiting the video's internal structure, for which it borrows proven heuristics from video compression; into the model, DeepCache propagates regions of reusable results by exploiting the model's internal structure. Notably, DeepCache eschews applying video heuristics to model internals which are not pixels but high-dimensional, difficult-to-interpret data. Our implementation of DeepCache works with unmodified deep learning models, requires zero developer's manual effort, and is therefore immediately deployable on off-the-shelf mobile devices. Our experiments show that DeepCache saves inference execution time by 18% on average and up to 47%. DeepCache reduces system energy consumption by 20% on average.

References

[1]

2015. How Google Translate squeezes deep learning onto a phone. https://research.googleblog.com/2015/07/ how-google-translate-squeezes-deep.html.

[2]

2016. Apple moves to third-generation Siri back-end, built on open-source Mesos platform. https://9to5mac.com/2015/04/27/ siri-backend-mesos/.

[3]

2016. TensorZoom App. https://play.google.com/store/apps/details? id=uk.tensorzoom&hl=en.

[4]

2017. Amazon App. https://itunes.apple.com/us/app/ amazon-app-shop-scan-compare/id297606951?mt=8.

[5]

2017. Autopilot-TensorFlow. https://github.com/SullyChen/ Autopilot-TensorFlow.

[6]

2017. Caffe2 deep learning framework. https://github.com/caffe2/ caffe2.

[7]

2017. Concat Layer. http://caffe.berkeleyvision.org/tutorial/layers/ concat.html.

[8]

2017. ffmpeg: a video processing platform. https://www.ffmpeg.org/.

[9]

2017. HTTP Caching. https://developers.google.com/web/ fundamentals/performance/optimizing-content-efficiency/ http-caching.

[10]

2017. Local Response Normalization (LRN). http://caffe.berkeleyvision. org/tutorial/layers/lrn.html.

[11]

2017. Ncnn: a high-performance neural network inference framework. https://github.com/Tencent/ncnn.

[12]

2017. Nvidia driving dataset. https://drive.google.com/file/d/ 0B-KJCaaF7elleG1RbzVPZWV4Tlk/view?usp=sharing.

[13]

2017. Peak signal-to-noise ratio. https://en.wikipedia.org/wiki/Peak_ signal-to-noise_ratio.

[14]

2017. RenderScript. https://developer.android.com/guide/topics/ renderscript/compute.html.

[15]

2017. Snapdragon Profiler. https://developer.qualcomm.com/software/ snapdragon-profiler.

[16]

2017. Softmax Layer. http://caffe.berkeleyvision.org/tutorial/layers/ softmax.html.

[17]

2017. TensorFlow. https://www.tensorflow.org/.

[18]

2017. TensorFlow Android Camera Demo. https://github.com/ tensorflow/tensorflow/tree/master/tensorflow/examples/android.

[19]

2017. The PASCAL Visual Object Classes. http://host.robots.ox.ac.uk/ pascal/VOC/.

[20]

2018. Android MediaCodec. https://developer.android.com/reference/ android/media/MediaCodec.html.

[21]

Aroh Barjatya. 2004. Block matching algorithms for motion estimation. IEEE Transactions Evolution Computation 8, 3 (2004), 225--239.

Digital Library

[22]

Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, LawrenceDJackel, MathewMonfort, Urs Muller, Jiakai Zhang, et al. 2016. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016).

[23]

Mark Buckler, Philip Bedoukian, Suren Jayasuriya, and Adrian Sampson. 2018. EVA2: Exploiting Temporal Redundancy in Live Computer Vision. Proceedings of the 45th Annual International Symposium on Computer Architecture, ISCA'18 (2018).

Digital Library

[24]

Lukas Cavigelli, Philippe Degen, and Luca Benini. 2017. CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data. arXiv preprint arXiv:1704.04313 (2017).

[25]

Guoguo Chen, Carolina Parada, and Georg Heigold. 2014. Smallfootprint Keyword Spotting Using Deep Neural Networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'14). 4087--4091.

[26]

Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, and Olivier Temam. 2014. DianNao: a Small-footprint High-throughput Accelerator for Ubiquitous Machine-Learning. In Proceedings of the Architectural Support for Programming Languages and Operating Systems (ASPLOS'14). 269--284.

Digital Library

[27]

Tiffany Yu-Han Chen, Lenin Ravindranath, Shuo Deng, Paramvir Bahl, and Hari Balakrishnan. 2015. Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys'15). 155--168.

Digital Library

[28]

Yu-Hsin Chen, Joel S. Emer, and Vivienne Sze. 2016. Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks. In Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture, (ISCA'16). 367--379.

Digital Library

[29]

Zhuo Chen, Wenlu Hu, Junjue Wang, Siyan Zhao, Brandon Amos, Guanhang Wu, Kiryong Ha, Khalid Elgazzar, Padmanabhan Pillai, Roberta Klatzky, Daniel Siewiorek, and Mahadev Satyanarayanan. 2017. An Empirical Study of Latency in an Emerging Class of Edge Computing Applications for Wearable Cognitive Assistance. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing (SEC '17).

Digital Library

[30]

Anupam Das, Martin Degeling, Xiaoyou Wang, Junjue Wang, Norman M. Sadeh, and Mahadev Satyanarayanan. 2017. Assisting Users in aWorld Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR'17. 1387--1396.

[31]

Emily L. Denton,Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. 2014. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS'14). 1269--1277.

Digital Library

[32]

Peizhen Guo andWenjun Hu. 2018. Potluck: Cross-Application Approximate Deduplication for Computation-Intensive Mobile Applications. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems. 271--284.

Digital Library

[33]

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 Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture, (ISCA'16). 243--254.

Digital Library

[34]

Seungyeop Han, Haichen Shen, Matthai Philipose, Sharad Agarwal, Alec Wolman, and Arvind Krishnamurthy. 2016. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'16). 123--136.

Digital Library

[35]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16). 770-- 778.

[36]

Steve Hodges, Lyndsay Williams, Emma Berry, Shahram Izadi, James Srinivasan, Alex Butler, Gavin Smyth, Narinder Kapur, and Kenneth R. Wood. 2006. SenseCam: A Retrospective Memory Aid. In UbiComp 2006: Ubiquitous Computing, 8th International Conference, UbiComp 2006, Orange County, CA, USA, September 17--21, 2006. 177--193.

Digital Library

[37]

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).

[38]

Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 1. 3.

[39]

Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and <1MB Model Size. arXiv preprint arXiv:1602.07360 (2016).

[40]

Puneet Jain, Justin Manweiler, and Romit Roy Choudhury. 2015. Over- Lay: Practical Mobile Augmented Reality. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'15). 331--344.

Digital Library

[41]

Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Fei-Fei Li. 2014. Large-Scale Video Classification with Convolutional Neural Networks. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14). 1725--1732.

Digital Library

[42]

Kleomenis Katevas, Ilias Leontiadis, Martin Pielot, and Joan Serrà. 2017. Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions. In Proceedings of the 1st International Workshop on Embedded and Mobile Deep Learning (Deep Learning for Mobile Systems and Applications) (EMDL@MobiSys'17). 19--24.

Digital Library

[43]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS'12). 1106--1114.

Digital Library

[44]

Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim 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 2016). 23:1--23:12.

Digital Library

[45]

Nicholas D. Lane, Petko Georgiev, and Lorena Qendro. 2015. Deep- Ear: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'15). 283--294.

Digital Library

[46]

Didier Le Gall. 1991. MPEG: A video compression standard for multimedia applications. Commun. ACM 34, 4 (1991), 46--58.

Digital Library

[47]

Robert LiKamWa, Yunhui Hou, Yuan Gao, Mia Polansky, and Lin Zhong. 2016. RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision. In Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture ISCA'16. 255--266.

Digital Library

[48]

Robert LiKamWa, Bodhi Priyantha, Matthai Philipose, Lin Zhong, and Paramvir Bahl. 2013. Energy characterization and optimization of image sensing toward continuous mobile vision. In International Conference on Mobile Systems, Applications, and Services (MobiSys'13). 69--82.

Digital Library

[49]

Robert LiKamWa, Bodhi Priyantha, Matthai Philipose, Lin Zhong, and Paramvir Bahl. 2013. Energy proportional image sensors for continuous mobile vision. In International Conference on Mobile systems, Applications, and Services (MobiSys'13). 467--468.

Digital Library

[50]

Robert LiKamWa and Lin Zhong. 2015. Starfish: Efficient Concurrency Support for Computer Vision Applications. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'15). 213--226.

Digital Library

[51]

Huynh Nguyen Loc, Youngki Lee, and Rajesh Krishna Balan. 2017. DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'17). 82--95.

Digital Library

[52]

Akhil Mathur, Nicholas D. Lane, Sourav Bhattacharya, Aidan Boran, Claudio Forlivesi, and Fahim Kawsar. 2017. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'17). 68--81.

Digital Library

[53]

Zhonghong Ou, Changwei Lin, Meina Song, and Haihong E. 2017. A CNN-Based Supermarket Auto-Counting System. In Proceedings of the 17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP'17). 359--371.

[54]

Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16). 779--788.

[55]

Iain E Richardson. 2004. H. 264 and MPEG-4 video compression: video coding for next-generation multimedia.

[56]

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115, 3 (2015), 211--252.

Digital Library

[57]

Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild. arXiv preprint arXiv:1212.0402 (2012).

[58]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going Deeper with Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'15). 1--9.

[59]

Jo Yew Tham, Surendra Ranganath, Maitreya Ranganath, and Ashraf A Kassim. 1998. A novel unrestricted center-biased diamond search algorithm for block motion estimation. IEEE transactions on Circuits and Systems for Video Technology 8, 4 (1998), 369--377.

Digital Library

[60]

Ehsan Variani, Xin Lei, Erik McDermott, Ignacio Lopez-Moreno, and Javier Gonzalez-Dominguez. 2014. Deep Deural Detworks for Small Footprint Text-dependent Speaker Verification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'14). 4052--4056.

[61]

Tri Vu, Feng Lin, Nabil Alshurafa, and Wenyao Xu. 2017. Wearable Food Intake Monitoring Technologies: A Comprehensive Review. Computers 6 (2017).

[62]

Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. 2016. A Discriminative Feature Learning Approach for Deep Face Recognition. In Proceedings of the 14th European Conference on Computer Vision (ECCV'16). 499--515.

[63]

Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. 2016. Quantized Convolutional Neural Networks for Mobile Devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR'16). 4820--4828.

[64]

Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek F. Abdelzaher. 2017. DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing. In Proceedings of the 26th International Conference on World Wide Web, (WWW'17). 351--360.

Digital Library

[65]

Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, and Tarek Abdelzaher. 2017. DeepIoT: Compressing deep neural network structures for sensing systems with a compressor-critic framework. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM.

Digital Library

[66]

Xiao Zeng, Kai Cao, and Mi Zhang. 2017. MobileDeepPill: A Small- Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'17). 56--67.

Digital Library

[67]

Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong. 2015. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA'15). 161--170.

Digital Library

[68]

Shan Zhu and Kai-Kuang Ma. 1997. A new diamond search algorithm for fast block matching motion estimation. In Proceedings of the International Conference on Information, Communications and Signal Processing (ICICS'97). 292--296.

[69]

Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao, and Haitao Zheng. 2017. Object Recognition and Navigation using a Single Networking Device. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys'17). 265--277.

Digital Library

Cited By

Xu DZhang HYang LLiu RHuang GXu MLiu XEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)Fast On-device LLM Inference with NPUsProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707239(445-462)Online publication date: 3-Feb-2025
https://dl.acm.org/doi/10.1145/3669940.3707239
Wong MRamanujam MBalakrishnan GNetravali RVanbever LZhang I(2024)MadEyeProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691856(549-568)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691856
Chougule SChaudhari BGhorpade SZennaro M(2024)Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future DirectionsWorld Electric Vehicle Journal10.3390/wevj1502003915:2(39)Online publication date: 26-Jan-2024
https://doi.org/10.3390/wevj15020039
Show More Cited By

Index Terms

DeepCache: Principled Cache for Mobile Deep Vision
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
2. Human-centered computing
  1. Ubiquitous and mobile computing

Recommendations

DeepCache: A Deep Learning Based Framework For Content Caching
NetAI'18: Proceedings of the 2018 Workshop on Network Meets AI & ML

In this paper, we present DEEPCACHE a novel Framework for content caching, which can significantly boost cache performance. Our Framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object ...
Increasing hardware data prefetching performance using the second-level cache

Techniques to reduce or tolerate large memory latencies are critical for achieving high processor performance. Hardware data prefetching is one of the most heavily studied solutions, but it is essentially applied to first-level caches where it can ...
NCID: a non-inclusive cache, inclusive directory architecture for flexible and efficient cache hierarchies
CF '10: Proceedings of the 7th ACM international conference on Computing frontiers

Chip-multiprocessor (CMP) architectures employ multi-level cache hierarchies with private L2 caches per core and a shared L3 cache like Intel's Nehalem processor and AMD's Barcelona processor. When designing a multi-level cache hierarchy, one of the key ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

October 2018

884 pages

ISBN:9781450359030

DOI:10.1145/3241539

General Chairs:
Rajeev Shorey
TCS Innovation Labs, India
,
Rohan Murty
Soroco, USA/India
,
Program Chairs:
Yingying (Jennifer) Chen
WINLAB, Rutgers University, USA
,
Kyle Jamieson
Princeton University, USA

Copyright © 2018 ACM.

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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key R&D Program
National Natural Science Foundation of China

Conference

MobiCom '18

Sponsor:

SIGMOBILE

MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking

October 29 - November 2, 2018

New Delhi, India

Acceptance Rates

MobiCom '18 Paper Acceptance Rate 42 of 187 submissions, 22%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

135
Total Citations
View Citations
2,094
Total Downloads

Downloads (Last 12 months)206
Downloads (Last 6 weeks)20

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Xu DZhang HYang LLiu RHuang GXu MLiu XEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)Fast On-device LLM Inference with NPUsProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707239(445-462)Online publication date: 3-Feb-2025
https://dl.acm.org/doi/10.1145/3669940.3707239
Wong MRamanujam MBalakrishnan GNetravali RVanbever LZhang I(2024)MadEyeProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691856(549-568)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691856
Chougule SChaudhari BGhorpade SZennaro M(2024)Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future DirectionsWorld Electric Vehicle Journal10.3390/wevj1502003915:2(39)Online publication date: 26-Jan-2024
https://doi.org/10.3390/wevj15020039
Tang S(2024)Characterization and Optimization of Perception Deep Neural Networks on the Edge for Connected Autonomous Vehiclesundefined10.12794/metadc2332581Online publication date: May-2024
https://doi.org/10.12794/metadc2332581
Zhu SVoigt TRahimian FKo J(2024)On-device Training: A First Overview on Existing SystemsACM Transactions on Sensor Networks10.1145/369600320:6(1-39)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1145/3696003
Siam SAhn HLiu LAlam SShen HCao ZShroff NKrishnamachari BSrivastava MZhang M(2024)Artificial Intelligence of Things: A SurveyACM Transactions on Sensor Networks10.1145/369063921:1(1-75)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1145/3690639
Sponner MWaschneck BKumar A(2024)Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep LearningACM Computing Surveys10.1145/365728356:10(1-40)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657283
Ma QYuan HHu ZWang XYang Z(2024)A Liquidity Analysis System for Large-scale Video Streams in the OilfieldACM Transactions on Sensor Networks10.1145/364922220:3(1-22)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3649222
Yuan JYang CCai DWang SYuan XZhang ZLi XZhang DMei HJia XWang SXu MGanesan DShi W(2024)Mobile Foundation Model as FirmwareProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649361(279-295)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649361
Niu WSanim MShu ZGuan JShen XYin MAgrawal GRen BTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on MobileProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3620666.3651384(916-931)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620666.3651384
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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

EPUB

View this article in ePub.

Figures

Tables

Media

View Table of Conten