2 citations · 2 across the 4 of their papers we have counts for
4 papers
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: 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…
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…
Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons
Florentia Afentaki, Paula Carolina Lozano Duarte, Georgios Zervakis +1
Printed electronics technology offers a cost-effectiveand fully-customizable solution to computational needs beyondthe capabilities of traditional silicon technologies, offering ad…