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SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/c2013-0-10285-1openalex-snapshot
Probability, Statistics, and Queueing Theory
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Deep Convolutional Neural Networks for pedestrian detection
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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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Deep Residual Learning for Image Recognition
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Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
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Machine Learning at Facebook: Understanding Inference at the Edge
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PREMA: A Predictive Multi-Task Scheduling Algorithm For Preemptible Neural Processing Units
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The Architectural Implications of Facebook's DNN-Based Personalized Recommendation
inferredcites →https://doi.org/10.1109/icaci.2018.8377522openalex-snapshot
Image semantic segmentation based on convolutional neural network and conditional random field
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
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Machine Learning for Android Malware Detection Using Permission and API Calls
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SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning
inferredcites →https://doi.org/10.1109/ipsn.2016.7460664openalex-snapshot
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
inferredcites →https://doi.org/10.1109/ivs.2012.6232219openalex-snapshot
3D LIDAR point cloud based intersection recognition for autonomous driving
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Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services
inferredcites →https://doi.org/10.1109/mcom.2017.1700246openalex-snapshot
Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
inferredcites →https://doi.org/10.1109/mcom.2018.1700298openalex-snapshot
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
inferredcites →https://doi.org/10.1109/mdat.2020.2968258openalex-snapshot
Neural Network Inference on Mobile SoCs
inferredcites →https://doi.org/10.1109/milcom.2018.8599746openalex-snapshot
Image Classification on the Edge for Fast Multi-Camera Object Tracking
inferredcites →https://doi.org/10.1109/mm.2020.2975764openalex-snapshot
Compute Solution for Tesla's Full Self-Driving Computer
inferredcites →https://doi.org/10.1109/pact.2019.00021openalex-snapshot
MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference
inferredcites →https://doi.org/10.1109/robio.2011.6181557openalex-snapshot
Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior
inferredcites →https://doi.org/10.1109/rtas48715.2020.000-8openalex-snapshot
Real-Time Object Detection System with Multi-Path Neural Networks
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Wireless sensor network based smart home: Sensor selection, deployment and monitoring
inferredcites →https://doi.org/10.1109/smartcloud42540.2017openalex-snapshot
2017 IEEE International Conference on Smart Cloud (SmartCloud)
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High-Throughput CNN Inference on Embedded ARM Big.LITTLE Multicore Processors
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Research and implementation of the context-aware middleware for controlling home appliances
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Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data
inferredcites →https://doi.org/10.1109/tsmc.2013.2252338openalex-snapshot
Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks
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Machine Learning Algorithms in Bipedal Robot Control
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Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference
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Video analytics from edge to server
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A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking
inferredcites →https://doi.org/10.2352/issn.2470-1173.2017.19.avm-023openalex-snapshot
Deep Reinforcement Learning framework for Autonomous Driving
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Octopus: SLO-Aware Progressive Inference Serving via Deep Reinforcement Learning in Multi-tenant Edge Cluster
inferred← citeshttps://doi.org/10.1007/978-3-032-06372-4_24openalex-snapshot
Inference Acceleration and Optimization of AI Compiler on Heterogeneous Computing Platforms
inferred← citeshttps://doi.org/10.1007/978-3-032-24923-4_3openalex-snapshot
ACCLAIM: Accelerating Long Context LLM Inference on Heterogeneous Edge Platforms
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Lyapunov-Guided KV Cache Control for Multi-tenant Edge LLM Servicing
inferred← citeshttps://doi.org/10.1007/978-981-96-0805-8_16openalex-snapshot
UELLM: A Unified and Efficient Approach for Large Language Model Inference Serving
inferred← citeshttps://doi.org/10.1016/j.future.2023.11.010openalex-snapshot
Optimized resource usage with hybrid auto-scaling system for knative serverless edge computing
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Concurrent service auto-scaling for Knative resource quota-based serverless system
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inferred← citeshttps://doi.org/10.1109/ccgrid59990.2024.00053openalex-snapshot
Leveraging Multi-Modal Data for Efficient Edge Inference Serving
inferred← citeshttps://doi.org/10.1109/ecnct70535.2026.11660886openalex-snapshot
Cloud-Edge Heterogeneous Resource Scheduling and Optimization for AI Inference Tasks
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/hpca56546.2023.10070943openalex-snapshot
Know Your Enemy To Save Cloud Energy: Energy-Performance Characterization of Machine Learning Serving
inferred← citeshttps://doi.org/10.1109/iccd63220.2024.00038openalex-snapshot
Interference-Aware DNN Serving on Heterogeneous Processors in Edge Systems
inferred← citeshttps://doi.org/10.1109/icdcs60910.2024.00110openalex-snapshot
Joint AI Task Allocation and Virtual Object Quality Manipulation for Improved MAR App Performance
inferred← citeshttps://doi.org/10.1109/icde60146.2024.00128openalex-snapshot
COUPLE: Orchestrating Video Analytics on Heterogeneous Mobile Processors
inferred← citeshttps://doi.org/10.1109/iwcmc65282.2025.11059653openalex-snapshot
Tail-Latency Aware Scheduler For Inference Workloads
inferred← citeshttps://doi.org/10.1109/qsw59989.2023.00021openalex-snapshot
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
inferred← citeshttps://doi.org/10.1109/sbac-pad66369.2025.00024openalex-snapshot
Accelerating GNN Inference via Automated Parallel Execution on Edge Heterogeneous Platforms
inferred← citeshttps://doi.org/10.1109/sose62363.2024.00008openalex-snapshot
Diffusing High-level SLO in Microservice Pipelines
inferred← citeshttps://doi.org/10.1109/tc.2025.3528125openalex-snapshot
SLOpt: Serving Real-Time Inference Pipeline With Strict Latency Constraint
inferred← citeshttps://doi.org/10.1109/tcad.2023.3314388openalex-snapshot
Ace-Sniper: Cloud–Edge Collaborative Scheduling Framework With DNN Inference Latency Modeling on Heterogeneous Devices
inferred← citeshttps://doi.org/10.1109/tcad.2024.3443706openalex-snapshot
Arch2End: Two-Stage Unified System-Level Modeling for Heterogeneous Intelligent Devices
inferred← citeshttps://doi.org/10.1109/tcss.2024.3423749openalex-snapshot
SLoB: Suboptimal Load Balancing Scheduling in Local Heterogeneous GPU Clusters for Large Language Model Inference
inferred← citeshttps://doi.org/10.1109/tmc.2025.3640384openalex-snapshot
Offloading-Aware Control of AI Task Allocation and Virtual Object Quality in MAR Apps
inferred← citeshttps://doi.org/10.1109/tnse.2026.3691846openalex-snapshot
Causality-Aware Dual Graph Approach for Multi-Cloud Networking Forecasting
inferred← citeshttps://doi.org/10.1109/tnsm.2024.3409701openalex-snapshot
BCEdge: SLO-Aware DNN Inference Services With Adaptive Batch-Concurrent Scheduling on Edge Devices
inferred← citeshttps://doi.org/10.1109/wowmom69805.2026.00031openalex-snapshot
CLEAR: Scheduling of Multi-Model Mobile Workloads on Chiplet Edge Platforms
inferred← citeshttps://doi.org/10.1145/3570361.3615749openalex-snapshot
COUPLE: Accelerating Video Analytics on Heterogeneous Mobile Processors
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PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Accelerators
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SPLIT: QoS-Aware DNN Inference on Shared GPU via Evenly-Sized Model Splitting
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Improving Data Reuse in NPU On-chip Memory with Interleaved Gradient Order for DNN Training
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Automated Backend Allocation for Multi-Model, On-Device AI Inference
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Deep Learning Workload Scheduling in GPU Datacenters: A Survey
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Automated Backend Allocation for Multi-Model, On-Device AI Inference
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ARM-CO-UP: ARM COoperative Utilization of Processors
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CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
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Automated Backend Allocation for Multi-Model, On-Device AI Inference
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Flex: Fast, Accurate DNN Inference on Low-Cost Edges Using Heterogeneous Accelerator Execution
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Accordion: A malleable pipeline scheduling approach for adaptive SLO-aware inference serving
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PyTorchSim: A Comprehensive, Fast, and Accurate NPU Simulation Framework
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Scheduling based on Block Features for Concurrent Inference with Unseen DNN Models on GPU
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An Edge-Based Reinforcement Learning Approach for Augmented Reality Apps in Dynamic Contexts
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SneakPeek: Data-Aware Model Selection and Scheduling for Inference Serving on the Edge
inferred← citeshttps://doi.org/10.1145/3776865.3776925openalex-snapshot
3OTSO: Three-Objective Task Scheduling Optimization for CPU-GPU Heterogeneous Cloud Environments
inferred← citeshttps://doi.org/10.1145/3800959openalex-snapshot
SynergAI: Edge-to-Cloud Synergy for Architecture-Driven High-Performance Orchestration
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WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching
inferred← citeshttps://doi.org/10.32604/cmc.2025.064346openalex-snapshot
Homomorphic Encryption for Machine Learning Applications with CKKS Algorithms: A Survey of Developments and Applications
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A Survey of Advancements in Scheduling Techniques for Efficient Deep Learning Computations on GPUs
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Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
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BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
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BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
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