3 papers
cs.LG2025
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,…
cs.AR2025
Late Breaking Results: Leveraging Approximate Computing for Carbon-Aware DNN Accelerators
Aikaterini Maria Panteleaki, Konstantinos Balaskas, Georgios Zervakis +2
The rapid growth of Machine Learning (ML) has increased demand for DNN hardware accelerators, but their embodied carbon footprint poses significant environmental challenges. This p…
cs.LG2025
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…