Target venues: system conferences (OSDI/SOSP/ATC/EuroSys/ASPLOS), network conferences (NSDI/SIGCOMM) and mobile conferences (MobiCom/MobiSys/SenSys/UbiComp).
We will keep maintaining this list :)
Note: Edge here refers to resource-constrained devices, not edge servers; AI here mostly refers to deep learning.
[MobiCom'20] Deep Learning Based Wireless Localization for Indoor Navigation
[MobiCom'20] SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
[MobiCom'20] Heimdall: Mobile GPU Coordination Platform for Augmented Reality Applications
[MobiCom'20] NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices
[MobiCom'20] OnRL: Improving Mobile Video Telephony via Online Reinforcement Learning
[ASPLOS'20] PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
[MobiSys‘20] Deep Compressive Offloading: Speeding up Neural Network Inference by Trading Edge Computation for Network Latency
[MobiSys‘20] Fast and scalable In-memory Deep Multitask Learning via Neural Weight Virtualization
[MobiSys‘20] MDLdroidLite: A Release-and-inhibit Control Approach to Resource-efficient Deep Neural Networks on Mobile Devices
[MobiSys'20] RF-net: A Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition
[SenSys'20] MobiPose: real-time multi-person pose estimation on mobile devices
[MobiCom'19] RNN-Based Room Scale Hand Motion Tracking
[MobiCom'19] MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
[EuroSys'19] µLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization
[SenSys‘19] DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction
[MobiCom'18] DeepCache: Principled Cache for Mobile Deep Vision
[MobiCom'18] NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision
[MobiCom'18] FoggyCache: Cross-Device Approximate Computation Reuse
[MobiSys‘18]On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework
[MobiSys‘18]FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
[MobiSys'17] Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading
[MobiSys'17] MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images
[MobiSys'17] DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware
[MobiSys'17] DeepMon: Building Mobile GPU Deep Learning Models for Continuous Vision Applications
[ASPLOS'17] Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
[Ubicomp'16] SpotGarbage: Smartphone App to Detect Garbage Using Deep Learning
[Ubicomp'15] DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning
[ASPLOS'20] Coterie: Exploiting Frame Similarity to Enable High-Quality Multiplayer VR on Commodity Mobile Devices
[MobiCom'19] Edge Assisted Real-time Object Detection for Mobile Augmented Reality
[EuroSys'19] Transparent AR Processing Acceleration at the Edge
[MobiSys‘20] EMO: Real-time Emotion Recognition From Single-eye Images for Resource-constrained Eyewear Devices
[MobiCom'20] CLIO: Enabling Automatic Compilation of Deep Learning Pipelines Across IoT and Cloud
[MobiCom'20] EagleEye: Wearable Camera-based Person Identification in Crowded Urban Spaces
[SigComm'20] Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
[MobiCom'19] Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision
[SenSys‘19] Neuro.ZERO: A Zero-energy Neural Network Accelerator for Embedded Sensing and Inference Systems
[Ubicomp‘19] Performance Characterization of Deep Learning Models for Breathing-based Authentication on Resource-Constrained Devices
[ASPLOS'18] SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
[SenSys‘17] DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
[MobiSys'17] Glimpse: A Programmable Early-Discard Camera Architecture for Continuous Mobile Vision
[Ubicomp'17] Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations
[MobiSys‘16] MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints
[SenSys‘16] Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables
[MobiSys‘20] Approximate Query Service on Autonomous IoT Cameras
[SenSys‘20] Ember: Energy Management of Batteryless Event Detection Sensors with Deep Reinforcement Learning
[ASPLOS'19] Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
[MobiCom'20] FaceRevelio: A Face Liveness Detection System for Smartphones with A Single Front Camera
[ASPLOS'20] DNNGuard: An Elastic Heterogeneous DNN Accelerator Architecture against Adversarial Attacks
[Ubicomp'20] Countering Acoustic Adversarial Attacks in Microphone-equipped mart Home Devices
[Ubicomp'19] DeepType: On-Device Deep Learning for Input Personalization Service with Minimal Privacy Concern
[Ubicomp'19] Keyboard Snooping from Mobile Phone Arrays with Mixed Convolutional and Recurrent Neural Networks
[MobiCom'19] Occlumency: Privacy-preserving Remote Deep-learning Inference Using SGX
[EuroSys'19] Forward and Backward Private Searchable Encryption with SGX
[MobiCom'20] Billion-scale Federated Learning on Mobile Clients: a submodel design with tunable privacy
[SenSys‘19] MetaSense: Few-shot Adaptation to Untrained Conditions in Deep Mobile Sensing
Another awesome paper list about Federated Learning