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Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables
Florentia Afentaki, Sri Sai Rakesh Nakkilla, Konstantinos Balaskas +6
Conventional stress monitoring relies on episodic, symptom-focused interventions, missing the need for continuous, accessible, and cost-efficient solutions. State-of-the-art approa…
Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits
Ilias Sertaridis, Spyridon Besias, Florentia Afentaki +2
Printed Electronics (PE) technology has emerged as a promising alternative to silicon-based computing. It offers attractive properties such as on-demand ultra-low-cost fabrication,…
Late Breaking Results: Energy-Efficient Printed Machine Learning Classifiers with Sequential SVMs
Spyridon Besias, Ilias Sertaridis, Florentia Afentaki +2
Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to lar…
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization
Konstantinos Balaskas, Andreas Karatzas, Christos Sad +4
Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential…