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Reaching for the Sky: Maximizing Deep Learning Inference Throughput on Edge Devices with AI Multi-Tenancy
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.future.2019.02.050openalex-snapshot
Edge computing: A survey
inferredcites →https://doi.org/10.1109/cvpr.1994.323798openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/iiswc.2005.1525988openalex-snapshot
Proceedings of the 2005 IEEE International Symposium on Workload Characterization
inferredcites →https://doi.org/10.1109/jiot.2016.2579198openalex-snapshot
Edge Computing: Vision and Challenges
inferredcites →https://doi.org/10.1109/jproc.2019.2915983openalex-snapshot
Edge Computing for Autonomous Driving: Opportunities and Challenges
inferredcites →https://doi.org/10.1109/jproc.2019.2918951openalex-snapshot
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
inferredcites →https://doi.org/10.1109/jproc.2019.2921977openalex-snapshot
Deep Learning With Edge Computing: A Review
inferredcites →https://doi.org/10.1109/mnet.2017.1700030openalex-snapshot
Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing
inferredcites →https://doi.org/10.1109/tasl.2008.919072openalex-snapshot
On the Importance of the Pearson Correlation Coefficient in Noise Reduction
inferredcites →https://doi.org/10.1109/twc.2019.2946140openalex-snapshot
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
inferredcites →https://doi.org/10.1145/3038228openalex-snapshot
Proceedings of the General Purpose GPUs
inferredcites →https://doi.org/10.18653/v1/2021.repl4nlp-1.31openalex-snapshot
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup
inferredcites →https://doi.org/10.48550/arxiv.1906.01878openalex-snapshot
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-48424-7_18openalex-snapshot
Octopus: SLO-Aware Progressive Inference Serving via Deep Reinforcement Learning in Multi-tenant Edge Cluster
inferred← citeshttps://doi.org/10.1007/s11227-025-07830-6openalex-snapshot
Reinforcement learning-based intelligent edge orchestration for IoT: insights into performance and profitability
inferred← citeshttps://doi.org/10.1007/s11227-026-08436-2openalex-snapshot
Quantum computing for edge AI: opportunities, challenges, and future research directions
inferred← citeshttps://doi.org/10.1007/s11554-026-01915-yopenalex-snapshot
Modeling throughput and power consumption for real-time concurrent vision application deployments on edge
inferred← citeshttps://doi.org/10.1016/j.autcon.2025.106297openalex-snapshot
Weighted adaptive active transfer learning for imbalanced multi-object classification in construction site imagery
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2024.102823openalex-snapshot
Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2025.103063openalex-snapshot
A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification
inferred← citeshttps://doi.org/10.1109/access.2023.3347548openalex-snapshot
Near-Edge Computing Aware Object Detection: A Review
inferred← citeshttps://doi.org/10.1109/access.2025.3534020openalex-snapshot
Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing
inferred← citeshttps://doi.org/10.1109/access.2025.3609798openalex-snapshot
QuantEdge: A Hybrid Quantization Approach for Optimized AI Deployment Across Edge Devices
inferred← citeshttps://doi.org/10.1109/cic58953.2023.00017openalex-snapshot
Toward Low-Cost and Sustainable IoT Systems for Soil Monitoring in Coastal Wetlands
inferred← citeshttps://doi.org/10.1109/edge62653.2024.00023openalex-snapshot
Characterizing Deep Learning Model Compression with Post-Training Quantization on Accelerated Edge Devices
inferred← citeshttps://doi.org/10.1109/edge67623.2025.00022openalex-snapshot
Convergo: Multi-SLO-Aware Scheduling for Heterogeneous AI Accelerators on Edge Devices
inferred← citeshttps://doi.org/10.1109/hipcw63042.2024.00068openalex-snapshot
Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device
inferred← citeshttps://doi.org/10.1109/iccd63220.2024.00076openalex-snapshot
Hermes: Memory-Efficient Pipeline Inference for Large Models on Edge Devices
inferred← citeshttps://doi.org/10.1109/icce70609.2026.11658392openalex-snapshot
Design, Implementation and Evaluation of an Edge-AI Cluster Architecture for Performance-Optimized AI Workloads
inferred← citeshttps://doi.org/10.1109/icfec69006.2026.00012openalex-snapshot
Coral: Covariance-Guided Resource Adaptive Learning for Efficient Edge Inference
inferred← citeshttps://doi.org/10.1109/icirca57980.2023.10220638openalex-snapshot
Multi-Tenancy Architecture for Augmented Security in Cloud Computing
inferred← citeshttps://doi.org/10.1109/ictc66702.2025.11389001openalex-snapshot
Quantum-Enhanced Edge Intelligence: Bridging Quantum Computing and Distributed AI
inferred← citeshttps://doi.org/10.1109/jiot.2024.3432763openalex-snapshot
Real-Time Tumor Detection Using Electromagnetic Signals With Memristive Echo State Networks
inferred← citeshttps://doi.org/10.1109/jiot.2025.3648721openalex-snapshot
CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models
inferred← citeshttps://doi.org/10.1109/nca61908.2024.00036openalex-snapshot
A Demand-aware Networked System Using Telemetry and ML with REACTNET
inferred← citeshttps://doi.org/10.1109/sera69989.2026.11618619openalex-snapshot
Enhancing Security in IoMT Healthcare Systems via AI-Based Intrusion Detection
inferred← citeshttps://doi.org/10.1145/3744339openalex-snapshot
Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
inferred← citeshttps://doi.org/10.1145/3769102.3772715openalex-snapshot
SEEB-GPU: Early-Exit Aware Scheduling and Batching for Edge GPU Inference
inferred← citeshttps://doi.org/10.1145/3845607openalex-snapshot
Hermes \({}^{+}\) : Adaptive Memory-Efficient Pipeline Inference for Large Models on Edge Devices
inferred← citeshttps://doi.org/10.1145/3847112openalex-snapshot
ProfEdge: Efficient Construction of DNN Performance Evaluation Model on Edge Devices
inferred← citeshttps://doi.org/10.3389/fpls.2023.1308528openalex-snapshot
Plant disease detection model for edge computing devices
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Systematic Review of Quantization-Optimized Lightweight Transformer Architectures for Real-Time Fruit Ripeness Detection on Edge Devices
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