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Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/aspdac.2018.8297274openalex-snapshot
Quantized deep neural networks for energy efficient hardware-based inference
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7299170openalex-snapshot
A convolutional neural network cascade for face detection
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.643openalex-snapshot
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning
inferredcites →https://doi.org/10.1109/cvpr35066.2017openalex-snapshot
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
inferredcites →https://doi.org/10.1109/jproc.2017.2761740openalex-snapshot
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
inferredcites →https://doi.org/10.1109/micro.2014.58openalex-snapshot
DaDianNao: A Machine-Learning Supercomputer
inferredcites →https://doi.org/10.1109/mm.2017.54openalex-snapshot
Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators
inferredcites →https://doi.org/10.1109/tc.2018.2889053openalex-snapshot
Learning-Based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
inferredcites →https://doi.org/10.1109/tcad.2016.2604288openalex-snapshot
Design-Space Exploration and Optimization of an Energy-Efficient and Reliable 3-D Small-World Network-on-Chip
inferredcites →https://doi.org/10.1109/tcad.2018.2857338openalex-snapshot
Trading-Off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach
inferredcites →https://doi.org/10.1109/tcsvt.2016.2589879openalex-snapshot
Cost-Effective Active Learning for Deep Image Classification
inferredcites →https://doi.org/10.1109/tit.2011.2182033openalex-snapshot
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
inferredcites →https://doi.org/10.1109/tvlsi.2017.2700726openalex-snapshot
Imitation Learning for Dynamic VFI Control in Large-Scale Manycore Systems
inferredcites →https://doi.org/10.1109/tvlsi.2019.2926106openalex-snapshot
Dynamic Resource Management of Heterogeneous Mobile Platforms via Imitation Learning
inferredcites →https://doi.org/10.1145/3240765.3240796openalex-snapshot
Designing adaptive neural networks for energy-constrained image classification
inferredcites →https://doi.org/10.23919/date.2018.8342068openalex-snapshot
ICNN: An iterative implementation of convolutional neural networks to enable energy and computational complexity aware dynamic approximation
inferredcites →https://doi.org/10.3850/9783981537079_0819openalex-snapshot
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
inferredcites →https://doi.org/10.3929/ethz-a-003856832openalex-snapshot
Evolutionary algorithms for multiobjective optimization: methods and applications
inferredcites →https://doi.org/10.48550/arxiv.1506.02626openalex-snapshot
Learning both Weights and Connections for Efficient Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1511.05467openalex-snapshot
Predictive Entropy Search for Multi-objective Bayesian Optimization
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1702.03044openalex-snapshot
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
inferredcites →https://doi.org/10.5555/2627435.2638577openalex-snapshot
Structured prediction via output space search
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Green Mobile App Development: Building Sustainable Products
inferred← citeshttps://doi.org/10.1007/978-3-031-50514-0_9openalex-snapshot
Intelligence Inference on IoT Devices
inferred← citeshttps://doi.org/10.1016/b978-0-443-33943-1.00010-1openalex-snapshot
Explainable Artificial Intelligence and sustainable computing in Machine Learning
inferred← citeshttps://doi.org/10.1016/j.jclepro.2022.135334openalex-snapshot
Do We Need Exotic Models? Engineering Metrics to Enable Green Machine Learning from Tackling Accuracy-Energy Trade-offs
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.129402openalex-snapshot
Deep learning based image classification for embedded devices: A systematic review
inferred← citeshttps://doi.org/10.1016/j.sysarc.2022.102431openalex-snapshot
Optimizing deep neural networks on intelligent edge accelerators via flexible-rate filter pruning
inferred← citeshttps://doi.org/10.1109/access.2021.3090196openalex-snapshot
User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing
inferred← citeshttps://doi.org/10.1109/dac18072.2020.9218525openalex-snapshot
PETNet: Polycount and Energy Trade-off Deep Networks for Producing 3D Objects from Images
inferred← citeshttps://doi.org/10.1109/ecce44975.2020.9235599openalex-snapshot
Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design
inferred← citeshttps://doi.org/10.1109/iccad51958.2021.9643444openalex-snapshot
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
inferred← citeshttps://doi.org/10.1109/iccad51958.2021.9643557openalex-snapshot
A General Hardware and Software Co-Design Framework for Energy-Efficient Edge AI
inferred← citeshttps://doi.org/10.1109/jiot.2022.3140465openalex-snapshot
AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices
inferred← citeshttps://doi.org/10.1109/jiot.2026.3669436openalex-snapshot
Machine Learning for Fault Detection in Wireless Sensor Networks: A Survey
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A Novel Learning Strategy for the Trade-Off Between Accuracy and Computational Cost: A Touch Modalities Classification Case Study
inferred← citeshttps://doi.org/10.1109/mdat.2021.3069952openalex-snapshot
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign
inferred← citeshttps://doi.org/10.1109/tcad.2020.3013050openalex-snapshot
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices
inferred← citeshttps://doi.org/10.1109/tcad.2021.3136815openalex-snapshot
Similarity-Aware CNN for Efficient Video Recognition at the Edge
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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring
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Edge Computing and Few-Shot Learning Featured Intelligent Framework in Digital Twin Empowered Mobile Networks
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Synergistically Exploiting CNN Pruning and HLS Versioning for Adaptive Inference on Multi-FPGAs at the Edge
inferred← citeshttps://doi.org/10.1145/3510831openalex-snapshot
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
inferred← citeshttps://doi.org/10.1145/3534580openalex-snapshot
Template Matching Based Early Exit CNN for Energy-efficient Myocardial Infarction Detection on Low-power Wearable Devices
inferred← citeshttps://doi.org/10.1145/3576842.3582375openalex-snapshot
Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI
inferred← citeshttps://doi.org/10.1145/3587038openalex-snapshot
Edge-AI-Driven Framework with Efficient Mobile Network Design for Facial Expression Recognition
inferred← citeshttps://doi.org/10.1145/3632410.3632427openalex-snapshot
Preference-Aware Constrained Multi-Objective Bayesian Optimization
inferred← citeshttps://doi.org/10.1145/3639475openalex-snapshot
Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society
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A Synthesis of Green Architectural Tactics for ML-Enabled Systems
inferred← citeshttps://doi.org/10.1145/3699512openalex-snapshot
Real-time Blood Pressure Prediction on Wearables with Edge-Based DNNs: A Co-Design Approach
inferred← citeshttps://doi.org/10.48175/ijarsct-29629openalex-snapshot
Architectural Intelligence Inspired by Vastu
inferred← citeshttps://doi.org/10.48550/arxiv.2103.12896openalex-snapshot
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms
inferred← citeshttps://doi.org/10.48550/arxiv.2103.15750openalex-snapshot
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
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