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20212026
most citedHardware Approximate Techniques for Deep Neural Network Accelerators: A Survey

137 citations · 424 across the 38 of their papers we have counts for

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11 papers · 1 filter

cs.AR2025

Design and Optimization of Mixed-Kernel Mixed-Signal SVMs for Flexible Electronics

Florentia Afentaki, Maha Shatta, Konstantinos Balaskas +3

Flexible Electronics (FE) have emerged as a promising alternative to silicon-based technologies, offering on-demand low-cost fabrication, conformality, and sustainability. However,…

cs.LG2025

Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables

Theofanis Vergos, Polykarpos Vergos, Mehdi B. Tahoori +1

Flexible electronics offer unique advantages for conformable, lightweight, and disposable healthcare wearables. However, their limited gate count, large feature sizes, and high sta…

eess.SP2025

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation

Vojtech Mrazek, Konstantinos Balaskas, Paula Carolina Lozano Duarte +3

Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low…

cs.LG2025

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…

cs.AR2025

Support Vector Machines Classification on Bendable RISC-V

Polykarpos Vergos, Theofanis Vergos, Florentia Afentaki +2

Flexible Electronics (FE) technology offers uniquecharacteristics in electronic manufacturing, providing ultra-low-cost, lightweight, and environmentally-friendly alternatives totr…

cs.AR2025

Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability

Aikaterini Maria Panteleaki, Konstantinos Balaskas, Georgios Zervakis +2

As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint…

cs.AR2025★ 1 cited

Computing with Printed and Flexible Electronics

Mehdi B. Tahoori, Emre Ozer, Georgios Zervakis +2

Printed and flexible electronics (PFE) have emerged as the ubiquitous solution for application domains at the extreme edge, where the demands for low manufacturing and operational…

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

Function Approximation Using Analog Building Blocks in Flexible Electronics

Paula Carolina Lozano Duarte, Aradhana Dube, Georgios Zervakis +2

Function approximation is crucial in Flexible Electronics (FE), where applications demand efficient computational techniques within strict constraints on size, power, and performan…

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…