Adaptive deep learning model selection on embedded systems

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · see citing works

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201 · see citing works

Deep Face Recognition

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479 · see citing works

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_32 · Cited by 4,408 · see citing works

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works

Striving for Simplicity: The All Convolutional Net

Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works

Deep Speech 2: End-to-End Speech Recognition in English and MandarinPreprint

Amodei, D., Anubhai, R., Battenberg, E., Case, C. T., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.02595 · Cited by 2,191 · see citing works

EIE: efficient inference engine on compressed deep neural network

Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., & Dally, W. J. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001163 · Cited by 1,937 · see citing works

Deep Learning Face Representation by Joint Identification-Verification

Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906 · see citing works

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., & Dally, W. J. (2016) · 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca.2016.30 · Cited by 1,077 · see citing works

Compressing Neural Networks with the Hashing Trick

Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv · doi:10.48550/arxiv.1504.04788 · Cited by 974 · see citing works

An Analysis of Deep Neural Network Models for Practical Applications

Canziani, A., Paszke, A., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1605.07678 · Cited by 969 · see citing works

Untitled work

(2009) · neural information processing systems · Cited by 962 · see citing works

Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices

Teerapittayanon, S., McDanel, B., & Kung, H. T. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.226 · Cited by 843 · see citing works

Quasar

Delimitrou, C., & Kozyrakis, C. (2014) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2541940.2541941 · Cited by 704 · see citing works

Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3037697.3037698 · Cited by 673 · see citing works

Bilingual Word Embeddings for Phrase-Based Machine Translation

Zou, W. Y., Socher, R., Cer, D. M., & Manning, C. D. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1141 · Cited by 529 · see citing works

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large DatasetsPreprint

Klein, A., Falkner, S., Bartels, S., Hennig, P., & Hutter, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07079 · Cited by 424 · see citing works

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

Huynh, L. N., Lee, Y., & Balan, R. K. (2017) · Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081360 · Cited by 329 · see citing works

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural NetworksPreprint

Rastegari, M., Ordóñez, V., Redmon, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05279 · Cited by 279 · see citing works

Flattened Convolutional Neural Networks for Feedforward AccelerationPreprint

Jin, J., Dundar, A., & Culurciello, E. (2014) · International Conference on Learning Representations · doi:10.48550/arxiv.1412.5474 · Cited by 268 · see citing works

Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

Bhattacharya, S., & Lane, N. D. (2016) · Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems CD-ROM · doi:10.1145/2994551.2994564 · Cited by 236 · see citing works

Image Features Detection, Description and Matching

Hassaballah, M., Abdelmgeid, A., & Alshazly, H. A. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-28854-3_2 · Cited by 225 · see citing works

Factorized Convolutional Neural Networks

Wang, M., Liu, B., & Foroosh, H. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.71 · Cited by 210 · see citing works

Towards a holistic approach to auto-parallelization

Tournavitis, G., Wang, Z., Franke, B., & O’boyle, M. F. P. (2009) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/1542476.1542496 · Cited by 204 · see citing works

Machine Learning in Compiler Optimization

Wang, Z., & O’boyle, M. F. P. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2817118 · Cited by 193 · see citing works

Mapping parallelism to multi-cores

Wang, Z., & O’Boyle, M. (2009) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/1504176.1504189 · Cited by 189 · see citing works

End-to-End Deep Learning of Optimization Heuristics

Cummins, C., Petoumenos, P., Wang, Z., & Leather, H. (2017) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1109/pact.2017.24 · Cited by 188 · see citing works

Portable mapping of data parallel programs to OpenCL for heterogeneous systems

Grewe, D., Wang, Z., & O’Boyle, M. (2013) · IEEE/ACM International Symposium on Code Generation and Optimization (CGO) · doi:10.1109/cgo.2013.6494993 · Cited by 145 · see citing works

CNNdroid

Oskouei, S. S. L., Golestani, H. B., Hashemi, M., & Ghiasi, S. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2973801 · Cited by 110 · see citing works

Partitioning streaming parallelism for multi-cores

Wang, Z., & O’Boyle, M. (2010) · doi:10.1145/1854273.1854313 · Cited by 109 · see citing works

Towards Pervasive and User Satisfactory CNN across GPU Microarchitectures

Song, M., Hu, Y., Chen, H., & Li, T. (2017) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2017.52 · Cited by 77 · see citing works

Integrating profile-driven parallelism detection and machine-learning-based mapping

Wang, Z., Tournavitis, G., Franke, B., & O’boyle, M. F. P. (2014) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2579561 · Cited by 72 · see citing works

Low-resource multi-task audio sensing for mobile and embedded devices via shared deep neural network representations

Georgiev, P., Bhattacharya, S., Lane, N. D., & Mascolo, C. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3131895 · Cited by 67 · see citing works

OpenCL Task Partitioning in the Presence of GPU Contention

Grewe, D., Wang, Z., & O’Boyle, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09967-5_5 · Cited by 64 · see citing works

Smart, adaptive mapping of parallelism in the presence of external workload

Emani, M. K., Wang, Z., & O’Boyle, M. (2013) · IEEE/ACM International Symposium on Code Generation and Optimization (CGO) · doi:10.1109/cgo.2013.6495010 · Cited by 62 · see citing works

Optimise web browsing on heterogeneous mobile platforms: A machine learning based approach

Ren, J., Gao, L. C., Wang, H., & Wang, Z. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057087 · Cited by 55 · see citing works

ACAM

Imani, M., Kim, Y., Rahimi, A., & Rosing, T. (2016) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/2934583.2934595 · Cited by 54 · see citing works

ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural ProjectionsPreprint

Ravi, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00630 · Cited by 54 · see citing works

A workload-aware mapping approach for data-parallel programs

Grewe, D., Wang, Z., & O’Boyle, M. (2011) · doi:10.1145/1944862.1944881 · Cited by 53 · see citing works

Automatic and Portable Mapping of Data Parallel Programs to OpenCL for GPU-Based Heterogeneous Systems

Wang, Z., Grewe, D., & O’Boyle, M. (2014) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2677036 · Cited by 52 · see citing works

Using machine learning to improve automatic vectorization

Stock, K., Pouchet, L.-N., & Sadayappan, P. (2012) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2086696.2086729 · Cited by 51 · see citing works

Minimizing the cost of iterative compilation with active learning

Ogilvie, W. F., Petoumenos, P., Wang, Z., & Leather, H. (2017) · Lancaster EPrints (Lancaster University) · doi:10.5555/3049832.3049859 · Cited by 51 · see citing works

Daleel: Simplifying cloud instance selection using machine learning

Samreen, F., Elkhatib, Y., Rowe, M., & Blair, G. S. (2016) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2016.7502858 · Cited by 50 · see citing works

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

Taylor, B., Marco, V. S., & Wang, Z. (2017) · ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES) · doi:10.1145/3078633.3081040 · Cited by 49 · see citing works

Using machine learning to partition streaming programs

Wang, Z., & O’Boyle, M. (2013) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/2512436 · Cited by 46 · see citing works

Integrating Algorithmic Parameters into Benchmarking and Design Space Exploration in 3D Scene Understanding

Bodin, B., Nardi, L., Zia, M. Z., Wagstaff, H., Shenoy, G. S., Emani, M. K., Mawer, J., Kotselidis, C., Nisbet, A. P., Luján, M., et al. (2016) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1145/2967938.2967963 · Cited by 45 · see citing works

Minimizing the cost of iterative compilation with active learning

Ogilvie, W. F., Petoumenos, P., Wang, Z., & Leather, H. (2017) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo.2017.7863744 · Cited by 42 · see citing works

Fast Automatic Heuristic Construction Using Active Learning

Ogilvie, W. F., Petoumenos, P., Wang, Z., & Leather, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17473-0_10 · Cited by 40 · see citing works

Auto-tuning Streamed Applications on Intel Xeon Phi

Zhang, P., Fang, J., Tang, T., Yang, C., & Wang, Z. (2018) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2018.00061 · Cited by 40 · see citing works

Adaptive Optimization of Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Chen, S., Fang, J., Chen, D., Xu, C., & Wang, Z. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00116 · Cited by 38 · see citing works

Machine Intelligence on Resource-Constrained IoT Devices

Motamedi, M., Fong, D. D., & Ghiasi, S. (2017) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3126555 · Cited by 37 · see citing works

Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments

Emani, M. K., & O’Boyle, M. (2015) · Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/2737924.2737999 · Cited by 34 · see citing works

Are Very Deep Neural Networks Feasible on Mobile Devices

Rallapalli, S., Qiu, H., Bency, A. J., Karthikeyan, S., Govindan, R., Manjunath, B. S., & Urgaonkar, R. (2016) · Cited by 28 · see citing works

Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" StructurePreprint

Wang, M., Liu, B., & Foroosh, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04337 · Cited by 28 · see citing works

Personal Model Training under Privacy Constraints.

Servia-Rodríguez, S., Wang, L., Zhao, J., Mortier, R., & Haddadi, H. (2017) · Cited by 23 · see citing works

Improving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts ApproachPreprint

Marco, V. S., Taylor, B., Porter, B., & Wang, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.00610 · Cited by 16 · see citing works

Cloud-based or On-device: An Empirical Study of Mobile Deep InferencePreprint

Guo, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.04610 · Cited by 3 · see citing works

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455 · see citing works

Deep Learning With Edge Computing: A Review

Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540 · see citing works

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works

Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

Aldoseri, A., Al‐Khalifa, K. N., & Hamouda, A. M. S. (2023) · Applied Sciences · doi:10.3390/app13127082 · Cited by 838 · see citing works

Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge

Hu, C., Bao, W., Wang, D., & Liu, F. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737614 · Cited by 416 · see citing works

SPINN

Laskaridis, S., Venieris, S. I., Almeida, M., Ilias Leontiadis, A., & Lane, N. D. (2020) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3372224.3419194 · Cited by 319 · see citing works

A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform

Mittal, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.01.011 · Cited by 295 · see citing works

Edge Intelligence: Empowering Intelligence to the Edge of Network

Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 281 · see citing works

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Ajani, T. S., Imoize, A. L., & Atayero, A. A. A. (2021) · Sensors · doi:10.3390/s21134412 · Cited by 185 · see citing works

Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint

Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 184 · see citing works

Adaptive Inference through Early-Exit Networks

Laskaridis, S., Kouris, A., & Lane, N. D. (2021) · International Workshop on Embedded and Mobile Deep Learning (EMDL) · doi:10.1145/3469116.3470012 · Cited by 128 · see citing works

An Overview of Efficient Interconnection Networks for Deep Neural Network Accelerators

Nabavinejad, S. M., Baharloo, M. M., Chen, K.-C. J., Palesi, M., Kogel, T., & Ebrahimi, M. (2020) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2020.3022920 · Cited by 84 · see citing works

Smart City System Design

Habibzadeh, H., Kaptan, C., Soyata, T., Kantarcı, B., & Boukerche, A. (2019) · ACM Computing Surveys · doi:10.1145/3309545 · Cited by 83 · see citing works

Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection

Marco, V. S., Taylor, B., Wang, Z., & Elkhatib, Y. (2020) · Lancaster EPrints (Lancaster University) · Cited by 78 · see citing works

Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference

Koubaa, A., Ammar, A., Kanhouch, A., & AlHabashi, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3055835 · Cited by 69 · see citing works

DNN Surgery: Accelerating DNN Inference on the Edge Through Layer Partitioning

Liang, H., Sang, Q., Hu, C., Cheng, D., Zhou, X., Wang, D., Bao, W., & Wang, Y. (2023) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2023.3258982 · Cited by 67 · see citing works

Reconfigurable Intelligent Surface for Green Edge Inference

Hua, S., Zhou, Y., Yang, K., Shi, Y., & Wang, K. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3058657 · Cited by 74 · see citing works

Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles

de Prado, M., Rusci, M., Capotondi, A., Donze, R., Benini, L., & Pazos, N. (2021) · Sensors · doi:10.3390/s21041339 · Cited by 57 · see citing works

Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

Lopez-Montiel, M. A., Orozco-Rosas, U., Sánchez-Adame, M., Picos, K., & Montiel, O. (2021) · IEEE Access · doi:10.1109/access.2021.3097969 · Cited by 55 · see citing works

Joint Optimization of DNN Partition and Scheduling for Mobile Cloud Computing

Duan, Y., & Wu, J. (2021) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3472456.3472468 · Cited by 47 · see citing works

Optimizing Job Offloading Schedule for Collaborative DNN Inference

Duan, Y., & Wu, J. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3276937 · Cited by 47 · see citing works

HAPI

Laskaridis, S., Venieris, S. I., Kim, H., & Lane, N. D. (2020) · doi:10.1145/3400302.3415698 · Cited by 42 · see citing works

Adaptive Optimization of Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Chen, S., Fang, J., Chen, D., Xu, C., & Wang, Z. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00116 · Cited by 38 · see citing works

Online Power Management for Multi-Cores: A Reinforcement Learning Based Approach

Wang, Y., Zhang, W., Hao, M., & Wang, Z. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3092270 · Cited by 36 · see citing works

Proteus

Ren, J., Wang, X., Fang, J., Feng, Y., Zhu, D., Luo, Z., Zheng, J., & Wang, Z. (2018) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3281411.3281422 · Cited by 32 · see citing works

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network InferencePreprint

Hong, S., Kaya, Y., Modoranu, I.-V., & Dumitraş, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02432 · Cited by 39 · see citing works

CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks

Pagliari, D. J., Chiaro, R., Macii, E., & Poncino, M. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.3021199 · Cited by 30 · see citing works

To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference

Qin, Q., Ren, J., Yu, J., Wang, H., Gao, L. C., Zheng, J., Feng, Y., Fang, J., & Wang, Z. (2018) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/bdcloud.2018.00110 · Cited by 29 · see citing works

Optimizing Sparse Matrix–Vector Multiplications on an ARMv8-based Many-Core Architecture

Chen, D., Fang, J., Chen, S., Xu, C., & Wang, Z. (2019) · International Journal of Parallel Programming · doi:10.1007/s10766-018-00625-8 · Cited by 27 · see citing works

Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach

Ngo, M. V., Luo, T., & Quek, T. Q. S. (2021) · ACM Transactions on Internet of Things · doi:10.1145/3480172 · Cited by 26 · see citing works

Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Tian, X., Zhu, J., Xu, T., & Li, Y. (2021) · Sensors · doi:10.3390/s21010229 · Cited by 24 · see citing works

Optimizing computational resources for edge intelligence through model cascade strategies

Gomez-Carmona, O., Casado-Mansilla, D., Lopez-de-Ipina, D., & Garcia-Zubia, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3118845 · Cited by 21 · see citing works

Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

Stylianopoulos, K., Merluzzi, M., Lorenzo, P. D., & Alexandropoulos, G. C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095112 · Cited by 20 · see citing works

Automating Deep Neural Network Model Selection for Edge Inference

Lu, B., Yang, J., Chen, L. Y., & Ren, S. (2019) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi48466.2019.00035 · Cited by 18 · see citing works

Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Ren, J., Yuan, L., Nurmi, P., Wang, X., Ma, M., Gao, L. C., Tang, Z., Zheng, J., & Wang, Z. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155489 · Cited by 18 · see citing works

Dynamic Sharing in Multi-accelerators of Neural Networks on an FPGA Edge Device

Ting, H.-Y., Giyahchi, T., Sani, A. A., & Bozorgzadeh, E. (2020) · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap49362.2020.00040 · Cited by 17 · see citing works

Edge Intelligence: Challenges and Opportunities

Hu, H., & Jiang, C. (2020) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits49457.2020.9232575 · Cited by 16 · see citing works

Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI

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Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint

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Adaptive Anomaly Detection for IoT Data in Hierarchical Edge ComputingPreprint

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The Case for Adaptive Deep Neural Networks in Edge Computing

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Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

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Auto-Tuning MPI Collective Operations on Large-Scale Parallel Systems

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Contention Grading and Adaptive Model Selection for Machine Vision in Embedded Systems

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Memory-Throughput Trade-off for CNN-Based Applications at the Edge

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SLEXNet: Adaptive Inference Using Slimmable Early Exit Neural Networks

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Large Language Models on Small Resource-Constrained Systems: Performance Analysis and Trade-Offs

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Improving QoE of Deep Neural Network Inference on Edge Devices: A Bandit Approach

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Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection

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Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems

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A Structured Inference Optimization Approach for Vision-Based DNN Deployment on Legacy Systems

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Optimal Input-Dependent Edge-Cloud Partitioning for RNN Inference

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A Literature Review on Embedded Systems

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Enabling Adaptive Intelligence in Cloud-Augmented Multiple Robots Systems

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Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

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A Machine Learning Based Approach to Crack Detection in Asphalt Pavements

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Generic Architecture for Edge Computing Based on SPF for Military HADR Operations

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Adaptive Hardware Reconfiguration for Performance Tradeoffs in CNNs

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Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks

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NeuE: Automated Neural Network Ensembles for Edge Intelligence

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Inference Performance Comparison of Convolutional Neural Networks on Edge Devices

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A Flexible Implementation Model for Neural Networks on FPGAs

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Multi-Sensory CNN Models for Close Proximity Satellite Operations

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Run-time Deep Model Multiplexing

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Contention-aware Adaptive Model Selection for Machine Vision in Embedded Systems

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A Cascade Collaborative Offloading Framework for Video Analytics Based on Online Learning

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Timing Channels in Adaptive Neural Networks

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FrameFeedback: A Closed-Loop Control System for Dynamic Offloading Real-Time Edge Inference

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ATO-EDGE: Adaptive Task Offloading for Deep Learning in Resource-Constrained Edge Computing Systems

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Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Multi-coresPreprint

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OPTIMIZING RECOMMENDATIONS FOR CLUSTERING ALGORITHMS USING META-LEARNING

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Parallel-Pattern Aware Compiler Optimisations:Challenges and Opportunities

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Smart, Adaptive Energy Optimization for Mobile Web InteractionsPreprint

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Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-NetworkPreprint

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Artificial Intelligence Inference in Edge

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Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

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Machine Learning-Based Error Detection and Function Approximation for Reliable Computing

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Evaluation of Machine Learning Models for Enhancing System Reliability

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A new intelligent model selection approach

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MemSpec: Memory-Aware Runtime for Adaptive Draft Scheduling in Speculative Decoding on Edge Devices

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Adaptive Selection of Deep Learning Models on Embedded SystemsPreprint

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