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Awesome-EdgeAI
Resources of our survey paper "A Systematic Review of AI Deployment on Resource-Constrained Edge Devices: Challenges, Techniques, and Applications"
https://github.com/wangxb96/Awesome-EdgeAI
Last synced: 3 days ago
JSON representation
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1. Background Knowledge
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1.1. Edge Computing
- What is edge computing? Everything you need to know
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
- Machine Learning at the Edge — μML
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1.2. Edge AI
- Edge AI – What is it and how does it Work?
- What is Edge AI?
- Edge Intelligence: Edge Computing and Machine Learning (2023 Guide)
- What is Edge AI, and how does it work?
- Edge AI 101- What is it, Why is it important, and How to implement Edge AI?
- Edge AI: The Future of Artificial Intelligence
- What is Edge AI? Machine Learning + IoT
- What is edge AI computing?
- 边缘计算 | 在移动设备上部署深度学习模型的思路与注意点
- 在边缘实现机器学习都需要什么？
- Edge AI – Driving Next-Gen AI Applications in 2022
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2. Our Survey (To be released)
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3. The Data-Model-System Optimization Triad
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3.1. Data Optimization
- Data
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Locomotion mode recognition using sensory data with noisy labels: A deep learning approach. IEEE Trans. on Mobile Computing.
- Big data cleaning based on mobile edge computing in industrial sensor-cloud[J - - |
- Federated data cleaning: Collaborative and privacy-preserving data cleaning for edge intelligence[J - - |
- A data stream cleaning system using edge intelligence for smart city industrial environments[J - - |
- Protonn: Compressed and accurate knn for resource-scarce devices[C
- Accessible melanoma detection using smartphones and mobile image analysis[J - - |
- ActID: An efficient framework for activity sensor based user identification[J - Clear Lake | -- |
- Descriptor Scoring for Feature Selection in Real-Time Visual Slam[C - - |
- Edge2Analysis: a novel AIoT platform for atrial fibrillation recognition and detection[J - Sen University | -- |
- Feature selection with limited bit depth mutual information for portable embedded systems[J - - |
- Seremas: Self-resilient mobile autonomous systems through predictive edge computing[C - - |
- "Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification." CVPR, 2013. - - |
- Toward intelligent sensing: Intermediate deep feature compression[J - - |
- Selective feature compression for efficient activity recognition inference[C - - |
- Video coding for machines: A paradigm of collaborative compression and intelligent analytics[J - - |
- Communication-computation trade-off in resource-constrained edge inference[J - step_framework) |
- Edge-based compression and classification for smart healthcare systems: Concept, implementation and evaluation[J - - |
- EFCam: Configuration-adaptive fog-assisted wireless cameras with reinforcement learning[C - - |
- "Deep-Learning Based Monitoring Of Fog Layer Dynamics In Wastewater Pumping Stations", Water research 202 (2021): 117482. - - |
- Distributed and efficient object detection via interactions among devices, edge, and cloud[J - - |
- An effective litchi detection method based on edge devices in a complex scene[J - - |
- Segmentation of drivable road using deep fully convolutional residual network with pyramid pooling[J - - |
- Multiuser physical layer authentication in internet of things with data augmentation[J - - |
- Data-augmentation-based cellular traffic prediction in edge-computing-enabled smart city[J - - |
- Towards light-weight and real-time line segment detection[C
- Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE[J - - |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Big data cleaning based on mobile edge computing in industrial sensor-cloud[J - - |
- Federated data cleaning: Collaborative and privacy-preserving data cleaning for edge intelligence[J - - |
- A data stream cleaning system using edge intelligence for smart city industrial environments[J - - |
- Accessible melanoma detection using smartphones and mobile image analysis[J - - |
- Edge2Analysis: a novel AIoT platform for atrial fibrillation recognition and detection[J - Sen University | -- |
- Seremas: Self-resilient mobile autonomous systems through predictive edge computing[C - - |
- Toward intelligent sensing: Intermediate deep feature compression[J - - |
- Locomotion mode recognition using sensory data with noisy labels: A deep learning approach. IEEE Trans. on Mobile Computing.
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Descriptor Scoring for Feature Selection in Real-Time Visual Slam[C - - |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- "Deep-Learning Based Monitoring Of Fog Layer Dynamics In Wastewater Pumping Stations", Water research 202 (2021): 117482. - - |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- CROWD: crow search and deep learning based feature extractor for classification of Parkinson’s disease[J - - |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
- Supervised compression for resource-constrained edge computing systems[C - matsubara/supervised-compression) |
- Active label cleaning for improved dataset quality under resource constraints[J - DeepLearning/tree/1606729c7a16e1bfeb269694314212b6e2737939/InnerEye-DataQuality) |
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3.2. Model Optimization
- Model
- Mobilenets: Efficient convolutional neural networks for mobile vision applications[J
- Mobilenetv2: Inverted residuals and linear bottlenecks[C - li14/mobilenetv2.pytorch) |
- Searching for mobilenetv3[C - sqlai/mobilenetv3) |
- Rethinking bottleneck structure for efficient mobile network design[C
- Mnasnet: Platform-aware neural architecture search for mobile[C
- Shufflenet: An extremely efficient convolutional neural network for mobile devices[C - model/ShuffleNet-Series/tree/master/ShuffleNetV1) |
- Shufflenet v2: Practical guidelines for efficient cnn architecture design[C - model/ShuffleNet-Series/tree/master/ShuffleNetV2) |
- Single path one-shot neural architecture search with uniform sampling[C - model/ShuffleNet-Series/tree/master/OneShot) |
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J
- Squeezenext: Hardware-aware neural network design[C - tensorflow) |
- Ghostnet: More features from cheap operations[C - noah/ghostnet) |
- Efficientnet: Rethinking model scaling for convolutional neural networks[C - PyTorch) |
- Efficientnetv2: Smaller models and faster training[C
- Efficientdet: Scalable and efficient object detection[C
- Condensenet: An efficient densenet using learned group convolutions[C
- Condensenet v2: Sparse feature reactivation for deep networks[C
- Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation[C
- Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network[C
- Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search[C - vision) |
- Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions[C - vision) |
- Fbnetv3: Joint architecture-recipe search using predictor pretraining[C - - |
- Pelee: A real-time object detection system on mobile devices[J - JunWang/Pelee) |
- Going deeper with convolutions[C - Inception) |
- Rethinking the inception architecture for computer vision[C - Networks/blob/master/ClassicNetwork/InceptionV3.py) |
- Inception-v4, inception-resnet and the impact of residual connections on learning[C - Networks/blob/master/ClassicNetwork/InceptionV4.py) |
- Xception: Deep learning with depthwise separable convolutions[C - cifar100/blob/master/models/xception.py) |
- Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[J - cvnets) |
- Lite transformer with long-short range attention[J - han-lab/lite-transformer) |
- Coordinate attention for efficient mobile network design[C
- ECA-Net: Efficient channel attention for deep convolutional neural networks[C
- Sa-net: Shuffle attention for deep convolutional neural networks[C - Net) |
- Resnest: Split-attention networks[C
- An adaptive neural architecture search design for collaborative edge-cloud computing[J - - |
- Binarized neural architecture search for efficient object recognition[J - - |
- Multiobjective reinforcement learning-based neural architecture search for efficient portrait parsing[J - - |
- Hardcore-nas: Hard constrained differentiable neural architecture search[C - MIIL/HardCoReNAS) |
- MemNAS: Memory-efficient neural architecture search with grow-trim learning[C - - |
- Pvnas: 3D neural architecture search with point-voxel convolution[J - - |
- Toward tailored models on private aiot devices: Federated direct neural architecture search[J - - |
- Automatic design of convolutional neural network architectures under resource constraints[J - - |
- Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[J - cvnets) |
- Lite transformer with long-short range attention[J - han-lab/lite-transformer) |
- Triplet Attention: Rethinking the Similarity in Transformers[C
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3.2.2. Model Compression
- Train big, then compress: Rethinking model size for efficient training and inference of transformers[C - - |
- Hrank: Filter pruning using high-rank feature map[C
- Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers[J - - |
- Deepadapter: A collaborative deep learning framework for the mobile web using context-aware network pruning[C - - |
- SCANN: Synthesis of compact and accurate neural networks[J - - |
- Directx: Dynamic resource-aware cnn reconfiguration framework for real-time mobile applications[J - - |
- Pruning deep reinforcement learning for dual user experience and storage lifetime improvement on mobile devices[J - - |
- SuperSlash: A unified design space exploration and model compression methodology for design of deep learning accelerators with reduced off-chip memory access volume[J - - |
- Penni: Pruned kernel sharing for efficient CNN inference[C
- Fast operation mode selection for highly efficient iot edge devices[J - - |
- Efficient on-chip learning for optical neural networks through power-aware sparse zeroth-order optimization[C - - |
- A Fast Post-Training Pruning Framework for Transformers[C - free-pruning) |
- Radio frequency fingerprinting on the edge[J - - |
- Exploring sparsity in image super-resolution for efficient inference[C
- Enabling on-device cnn training by self-supervised instance filtering and error map pruning[J - - |
- Dropnet: Reducing neural network complexity via iterative pruning[C
- Fusion-catalyzed pruning for optimizing deep learning on intelligent edge devices[J - - |
- 3D CNN acceleration on FPGA using hardware-aware pruning[C - - |
- Width & depth pruning for vision transformers[C - - |
- Prive-hd: Privacy-preserved hyperdimensional computing[C - - |
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3.2.2.2. Parameter Sharing
- Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions[C - K-Means) |
- T-basis: a compact representation for neural networks[C
- ShiftAddNAS: Hardware-inspired search for more accurate and efficient neural networks[C - EIC/ShiftAddNAS) |
- EfficientTDNN: Efficient architecture search for speaker recognition[J
- Neural architecture search for LF-MMI trained time delay neural networks[J - - |
- Structured transforms for small-footprint deep learning[J - - |
- "Soft Weight-Sharing for Neural Network Compression." International Conference on Learning Representations. - SoftWeightSharingForNNCompression) |
- A generic network compression framework for sequential recommender systems[C - nlp/CpRec) |
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3.2.2.3. Model Quantization
- Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient dnn training[J - EIC/FracTrain) |
- Q-capsnets: A specialized framework for quantizing capsule networks[C
- Fspinn: An optimization framework for memory-efficient and energy-efficient spiking neural networks[J - - |
- Octo: INT8 Training with Loss-aware Compensation and Backward Quantization for Tiny On-device Learning[C
- Hardware-centric automl for mixed-precision quantization[J - - |
- An automated quantization framework for high-utilization rram-based pim[J - - |
- Exact neural networks from inexact multipliers via fibonacci weight encoding[C - - |
- Integer-arithmetic-only certified robustness for quantized neural networks[C - - |
- Bits-Ensemble: Toward Light-Weight Robust Deep Ensemble by Bits-Sharing[J - - |
- Similarity-Aware CNN for Efficient Video Recognition at the Edge[J - - |
- Data-Free Network Compression via Parametric Non-uniform Mixed Precision Quantization[C - - |
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3.2.2.4. Knowledge Distillation
- Be your own teacher: Improve the performance of convolutional neural networks via self distillation[C - LinfengZhang/) |
- Dynabert: Dynamic bert with adaptive width and depth[J - noah/Pretrained-Language-Model/tree/master/DynaBERT) |
- Scan: A scalable neural networks framework towards compact and efficient models[J - LinfengZhang/pytorch-scalable-neural-networks) |
- Content-aware gan compression[C - - |
- Cross-modal knowledge distillation for vision-to-sensor action recognition[C - - |
- Learning efficient and accurate detectors with dynamic knowledge distillation in remote sensing imagery[J - - |
- Personalized edge intelligence via federated self-knowledge distillation[J - - |
- Mobilefaceswap: A lightweight framework for video face swapping[C - - |
- Dynamically pruning segformer for efficient semantic segmentation[C - - |
- CDFKD-MFS: Collaborative Data-Free Knowledge Distillation via Multi-Level Feature Sharing[J - MFS) |
- Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation[J - distillation) |
- Learning Accurate, Speedy, Lightweight CNNs via Instance-Specific Multi-Teacher Knowledge Distillation for Distracted Driver Posture Identification[J - - |
- Stochastic precision ensemble: self-knowledge distillation for quantized deep neural networks[C - - |
- On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation[C - Rec) |
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3.2.2.5. Low-rank Factorization
- Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification[C - - |
- MicroNet: Towards image recognition with extremely low FLOPs[J - - |
- MicroNet: Towards image recognition with extremely low FLOPs[J - - |
- Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis[J - - |
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3.3. System Optimization
- System
- SparkNoC: An energy-efficiency FPGA-based accelerator using optimized lightweight CNN for edge computing[J - - |
- Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices[C - - |
- ACG-engine: An inference accelerator for content generative neural networks[C - - |
- Haq: Hardware-aware automated quantization with mixed precision[C - - |
- A lightweight collaborative deep neural network for the mobile web in edge cloud[J - - |
- Enabling incremental knowledge transfer for object detection at the edge[C - - |
- DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning[C - - |
- An efficient GPU-accelerated inference engine for binary neural network on mobile phones[J - sen University | [Code](https://code.ihub.org.cn/projects/915/repository/PhoneBit) |
- RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning[C - - |
- A variational information bottleneck based method to compress sequential networks for human action recognition[C - - |
- EdgeDRNN: Recurrent neural network accelerator for edge inference[J - - |
- Structured pruning of recurrent neural networks through neuron selection[J - - |
- Dynamically hierarchy revolution: dirnet for compressing recurrent neural network on mobile devices[J - - |
- High-throughput cnn inference on embedded arm big. little multicore processors[J - - |
- SCA: a secure CNN accelerator for both training and inference[C - - |
- Blockgnn: Towards efficient gnn acceleration using block-circulant weight matrices[C - - |
- {Hardware/Software}{Co-Programmable} Framework for Computational {SSDs} to Accelerate Deep Learning Service on {Large-Scale} Graphs[C - - |
- Achieving full parallelism in LSTM via a unified accelerator design[C - - |
- Pasgcn: An reram-based pim design for gcn with adaptively sparsified graphs[J - - |
- Ncpu: An embedded neural cpu architecture on resource-constrained low power devices for real-time end-to-end performance[C - - |
- Reduct: Keep it close, keep it cool!: Efficient scaling of dnn inference on multi-core cpus with near-cache compute[C - - |
- FARNN: FPGA-GPU hybrid acceleration platform for recurrent neural networks[J - - |
- Apgan: Approximate gan for robust low energy learning from imprecise components[J - - |
- Pipelined data-parallel CPU/GPU scheduling for multi-DNN real-time inference[C - - |
- Deadline-based scheduling for GPU with preemption support[C - - |
- Energon: Toward Efficient Acceleration of Transformers Using Dynamic Sparse Attention[J - - |
- Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs[J - - |
- BitSystolic: A 26.7 TOPS/W 2b~ 8b NPU with configurable data flows for edge devices[J - - |
- PL-NPU: An Energy-Efficient Edge-Device DNN Training Processor With Posit-Based Logarithm-Domain Computing[J - - |
- O3BNN-R: An out-of-order architecture for high-performance and regularized BNN inference[J - - |
- Hidet: Task-mapping programming paradigm for deep learning tensor programs[C - org/hidet) |
- A unified optimization approach for cnn model inference on integrated gpus[C
- Edgeeye: An edge service framework for real-time intelligent video analytics[C - Madison | -- |
- Source compression with bounded dnn perception loss for iot edge computer vision[C - - |
- NeuLens: spatial-based dynamic acceleration of convolutional neural networks on edge[C - - |
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4. Important Surveys on Edge AI (Related to edge inference and model deployment)
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2. Papers
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2.1. Edge Computing
- Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), 637-646.
- Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D. S. (2016, November). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 20-26). IEEE.
- Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78-81.
- Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30-39.
- Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge computing: A survey. Future Generation Computer Systems, 97, 219-235.
- Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2017). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465.
- Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE communications surveys & tutorials, 19(4), 2322-2358.
- Liu, F., Tang, G., Li, Y., Cai, Z., Zhang, X., & Zhou, T. (2019). A survey on edge computing systems and tools. Proceedings of the IEEE, 107(8), 1537-1562.
- Premsankar, G., Di Francesco, M., & Taleb, T. (2018). Edge computing for the Internet of Things: A case study. IEEE Internet of Things Journal, 5(2), 1275-1284.
- Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., & Lv, W. (2019). Edge computing security: State of the art and challenges. Proceedings of the IEEE, 107(8), 1608-1631.
- Sonmez, C., Ozgovde, A., & Ersoy, C. (2018). Edgecloudsim: An environment for performance evaluation of edge computing systems. Transactions on Emerging Telecommunications Technologies, 29(11), e3493.
- Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE network, 32(1), 96-101.
- Hassan, N., Gillani, S., Ahmed, E., Yaqoob, I., & Imran, M. (2018). The role of edge computing in internet of things. IEEE communications magazine, 56(11), 110-115.
- Sun, X., & Ansari, N. (2016). EdgeIoT: Mobile edge computing for the Internet of Things. IEEE Communications Magazine, 54(12), 22-29.
- Pan, J., & McElhannon, J. (2017). Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal, 5(1), 439-449.
- Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019). Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 107(8), 1697-1716.
- Shi, W., Pallis, G., & Xu, Z. (2019). Edge computing [scanning the issue
- Chen, B., Wan, J., Celesti, A., Li, D., Abbas, H., & Zhang, Q. (2018). Edge computing in IoT-based manufacturing. IEEE Communications Magazine, 56(9), 103-109.
- Xiong, Z., Zhang, Y., Niyato, D., Wang, P., & Han, Z. (2018). When mobile blockchain meets edge computing. IEEE Communications Magazine, 56(8), 33-39.
- Porambage, P., Okwuibe, J., Liyanage, M., Ylianttila, M., & Taleb, T. (2018). Survey on multi-access edge computing for internet of things realization. IEEE Communications Surveys & Tutorials, 20(4), 2961-2991.
- Ahmed, E., Ahmed, A., Yaqoob, I., Shuja, J., Gani, A., Imran, M., & Shoaib, M. (2017). Bringing computation closer toward the user network: Is edge computing the solution?. IEEE Communications Magazine, 55(11), 138-144.
- Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., & Flinck, H. (2017). Mobile edge computing potential in making cities smarter. IEEE Communications Magazine, 55(3), 38-43.
- He, Q., Cui, G., Zhang, X., Chen, F., Deng, S., Jin, H., ... & Yang, Y. (2019). A game-theoretical approach for user allocation in edge computing environment. IEEE Transactions on Parallel and Distributed Systems, 31(3), 515-529.
- Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628-1656.
- Tran, T. X., Hajisami, A., Pandey, P., & Pompili, D. (2017). Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Communications Magazine, 55(4), 54-61.
- Lin, L., Liao, X., Jin, H., & Li, P. (2019). Computation offloading toward edge computing. Proceedings of the IEEE, 107(8), 1584-1607.
- Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22(4), 2462-2488.
- Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021). Resource scheduling in edge computing: A survey. IEEE Communications Surveys & Tutorials, 23(4), 2131-2165.
- Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials, 19(3), 1657-1681.
- Khan, L. U., Yaqoob, I., Tran, N. H., Kazmi, S. A., Dang, T. N., & Hong, C. S. (2020). Edge-computing-enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal, 7(10), 10200-10232.
- Chen, X., Shi, Q., Yang, L., & Xu, J. (2018). ThriftyEdge: Resource-efficient edge computing for intelligent IoT applications. IEEE network, 32(1), 61-65.
- Baktir, A. C., Ozgovde, A., & Ersoy, C. (2017). How can edge computing benefit from software-defined networking: A survey, use cases, and future directions. IEEE Communications Surveys & Tutorials, 19(4), 2359-2391.
- Zhang, Z., Zhang, W., & Tseng, F. H. (2019). Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques. IEEE network, 33(1), 70-76.
- Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent edge computing for IoT-based energy management in smart cities. IEEE network, 33(2), 111-117.
- Abdellatif, A. A., Mohamed, A., Chiasserini, C. F., Tlili, M., & Erbad, A. (2019). Edge computing for smart health: Context-aware approaches, opportunities, and challenges. IEEE Network, 33(3), 196-203.
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2.2. Edge AI
- Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020). Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8), 7457-7469.
- Liu, Y., Peng, M., Shou, G., Chen, Y., & Chen, S. (2020). Toward edge intelligence: Multiaccess edge computing for 5G and Internet of Things. IEEE Internet of Things Journal, 7(8), 6722-6747.
- Sodhro, A. H., Pirbhulal, S., & De Albuquerque, V. H. C. (2019). Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Transactions on Industrial Informatics, 15(7), 4235-4243.
- Li, E., Zeng, L., Zhou, Z., & Chen, X. (2019). Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Transactions on Wireless Communications, 19(1), 447-457.
- Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019). In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network, 33(5), 156-165.
- Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., ... & Hui, P. (2020). Edge intelligence: Architectures, challenges, and applications. arXiv preprint arXiv:2003.12172.
- Zhang, J., & Letaief, K. B. (2019). Mobile edge intelligence and computing for the internet of vehicles. Proceedings of the IEEE, 108(2), 246-261.
- Xiao, Y., Shi, G., Li, Y., Saad, W., & Poor, H. V. (2020). Toward self-learning edge intelligence in 6G. IEEE Communications Magazine, 58(12), 34-40.
- Zhang, K., Zhu, Y., Maharjan, S., & Zhang, Y. (2019). Edge intelligence and blockchain empowered 5G beyond for the industrial Internet of Things. IEEE network, 33(5), 12-19.
- Zhang, Y., Ma, X., Zhang, J., Hossain, M. S., Muhammad, G., & Amin, S. U. (2019). Edge intelligence in the cognitive Internet of Things: Improving sensitivity and interactivity. IEEE Network, 33(3), 58-64.
- Zhang, Y., Huang, H., Yang, L. X., Xiang, Y., & Li, M. (2019). Serious challenges and potential solutions for the industrial Internet of Things with edge intelligence. IEEE Network, 33(5), 41-45.
- Tang, H., Li, D., Wan, J., Imran, M., & Shoaib, M. (2019). A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence. IEEE Internet of Things Journal, 7(5), 4248-4259.
- Mills, J., Hu, J., & Min, G. (2019). Communication-efficient federated learning for wireless edge intelligence in IoT. IEEE Internet of Things Journal, 7(7), 5986-5994.
- Tang, S., Chen, L., He, K., Xia, J., Fan, L., & Nallanathan, A. (2022). Computational intelligence and deep learning for next-generation edge-enabled industrial IoT. IEEE Transactions on Network Science and Engineering.
- Lim, W. Y. B., Ng, J. S., Xiong, Z., Jin, J., Zhang, Y., Niyato, D., ... & Miao, C. (2021). Decentralized edge intelligence: A dynamic resource allocation framework for hierarchical federated learning. IEEE Transactions on Parallel and Distributed Systems, 33(3), 536-550.
- Zhang, W., Zhang, Z., Zeadally, S., Chao, H. C., & Leung, V. C. (2019). MASM: A multiple-algorithm service model for energy-delay optimization in edge artificial intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4216-4224.
- Muhammad, K., Khan, S., Palade, V., Mehmood, I., & De Albuquerque, V. H. C. (2019). Edge intelligence-assisted smoke detection in foggy surveillance environments. IEEE Transactions on Industrial Informatics, 16(2), 1067-1075.
- Su, X., Sperlì, G., Moscato, V., Picariello, A., Esposito, C., & Choi, C. (2019). An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Transactions on Industrial Informatics, 15(7), 4266-4275.
- Yang, B., Cao, X., Xiong, K., Yuen, C., Guan, Y. L., Leng, S., ... & Han, Z. (2021). Edge intelligence for autonomous driving in 6G wireless system: Design challenges and solutions. IEEE Wireless Communications, 28(2), 40-47.
- Dai, Y., Zhang, K., Maharjan, S., & Zhang, Y. (2020). Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond. IEEE Transactions on Vehicular Technology, 69(10), 12175-12186.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Feng, C., Yu, K., Aloqaily, M., Alazab, M., Lv, Z., & Mumtaz, S. (2020). Attribute-based encryption with parallel outsourced decryption for edge intelligent IoV. IEEE Transactions on Vehicular Technology, 69(11), 13784-13795.
- Feng, C., Yu, K., Aloqaily, M., Alazab, M., Lv, Z., & Mumtaz, S. (2020). Attribute-based encryption with parallel outsourced decryption for edge intelligent IoV. IEEE Transactions on Vehicular Technology, 69(11), 13784-13795.
- Chen, B., Wan, J., Lan, Y., Imran, M., Li, D., & Guizani, N. (2019). Improving cognitive ability of edge intelligent IIoT through machine learning. IEEE network, 33(5), 61-67.
- Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2019). Wireless network intelligence at the edge. Proceedings of the IEEE, 107(11), 2204-2239.
- Ghosh, A. M., & Grolinger, K. (2020). Edge-cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Transactions on Industrial Informatics, 17(3), 2191-2200.
- Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., & Huang, K. (2020). Toward an intelligent edge: Wireless communication meets machine learning. IEEE communications magazine, 58(1), 19-25.
- Yang, H., Wen, J., Wu, X., He, L., & Mumtaz, S. (2019). An efficient edge artificial intelligence multipedestrian tracking method with rank constraint. IEEE Transactions on Industrial Informatics, 15(7), 4178-4188.
- Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020). Communication-efficient edge AI: Algorithms and systems. IEEE Communications Surveys & Tutorials, 22(4), 2167-2191.
- Soro, S. (2021). TinyML for ubiquitous edge AI. arXiv preprint arXiv:2102.01255.
- Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2021). Edge artificial intelligence for 6G: Vision, enabling technologies, and applications. IEEE Journal on Selected Areas in Communications, 40(1), 5-36.
- Ke, R., Zhuang, Y., Pu, Z., & Wang, Y. (2020). A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Transactions on Intelligent Transportation Systems, 22(8), 4962-4974.
- Mahendran, J. K., Barry, D. T., Nivedha, A. K., & Bhandarkar, S. M. (2021). Computer vision-based assistance system for the visually impaired using mobile edge artificial intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2418-2427).
- Zawish, M., Davy, S., & Abraham, L. (2022). Complexity-driven cnn compression for resource-constrained edge ai. arXiv preprint arXiv:2208.12816.
- Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., ... & Yang, H. (2022). Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI. IEEE Transactions on Knowledge and Data Engineering.
- Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762.
- Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Ding, A. Y., Peltonen, E., Meuser, T., Aral, A., Becker, C., Dustdar, S., ... & Wolf, L. (2022). Roadmap for edge AI: a Dagstuhl perspective. ACM SIGCOMM Computer Communication Review, 52(1), 28-33.
- Marculescu, R., Marculescu, D., & Ogras, U. (2020, August). Edge AI: Systems design and ML for IoT data analytics. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3565-3566).
- Li, E., Zhou, Z., & Chen, X. (2018, August). Edge intelligence: On-demand deep learning model co-inference with device-edge synergy. In Proceedings of the 2018 Workshop on Mobile Edge Communications (pp. 31-36).
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Stäcker, L., Fei, J., Heidenreich, P., Bonarens, F., Rambach, J., Stricker, D., & Stiller, C. (2021). Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1015-1022).
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
- Al-Rakhami, M., Gumaei, A., Alsahli, M., Hassan, M. M., Alamri, A., Guerrieri, A., & Fortino, G. (2020). A lightweight and cost effective edge intelligence architecture based on containerization technology. World Wide Web, 23(2), 1341-1360.
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Categories
Sub Categories
3.1. Data Optimization
94
2.2. Edge AI
81
3.3. System Optimization
44
3.2. Model Optimization
44
1.1. Edge Computing
36
2.1. Edge Computing
35
3.2.2. Model Compression
20
3.2.2.4. Knowledge Distillation
14
3.2.2.3. Model Quantization
11
1.2. Edge AI
11
3.2.2.2. Parameter Sharing
8
3.2.2.5. Low-rank Factorization
4
2.3 The Edge AI Deployment Pipeline
1
2.1 The Taxonomy of the Discussed Topics
1
2.2 Edge AI Optimization Triad
1