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Found 5 papers, 0 papers with code
Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs
We present a combinatorial evaluation of power-performance-accuracy trade-offs of EEG applications at different approximation, power, and performance levels to provide insights into the disciplined tuning of approximation in EEG applications on embedded platforms.
Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms
Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements.
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics.
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings.
