137 citations · 424 across the 38 of their papers we have counts for
39 papers
Low-Power PLL-Based Clock Stabilization for Flexible IGZO AMS Systems
Paula Carolina Lozano Duarte, Georgios Zervakis, Mehdi Tahoori
Flexible electronics (FE) platforms rely on analog and mixed-signal (AMS) circuits - biosensors, readout front-ends, and analog-to-digital converters - that dominate both functiona…
Co-Optimization of Analog Kolmogorov-Arnold Networks for Low-Power Function Approximation in Flexible Electronics
Paula Carolina Lozano Duarte, Georgios Zervakis, Mehdi Tahoori +1
Wearable devices and Internet of Things (IoT) sensors require on-sensor processing of biosignals and environmental data, including computationally demanding operations such as nonl…
Co-Design of CNN Accelerators for TinyML using Approximate Matrix Decomposition
José Juan Hernández Morales, Georgios Mentzos, Frank Hannig +4
The paradigm shift towards local and on-device inference under stringent resource constraints is represented by the tiny machine learning (TinyML) domain. The primary goal of TinyM…
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,…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
Leveraging Highly Approximated Multipliers in DNN Inference
Georgios Zervakis, Fabio Frustaci, Ourania Spantidi +3
In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our ap…
Sequential Printed MLP Circuits for Super TinyML Multi-Sensory Applications
Gurol Saglam, Florentia Afentaki, Georgios Zervakis +1
Super-TinyML aims to optimize machine learning models for deployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require co…
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…
Design and In-training Optimization of Binary Search ADC for Flexible Classifiers
Paula Carolina Lozano Duarte, Florentia Afentaki, Georgios Zervakis +1
Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands o…
Synthesis of Resource-Efficient Superconducting Circuits with Clock-Free Alternating Logic
Jennifer Volk, Panagiotis Papanikolaou, Georgios Zervakis +1
Gate-level clocking, typical in traditional approaches to Single Flux Quantum (SFQ) technology, makes the effective synthesis of superconducting circuits a significant engineering…
Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks
Vojtech Mrazek, Argyris Kokkinis, Panagiotis Papanikolaou +5
Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from t…
Variability-Aware Approximate Circuit Synthesis via Genetic Optimization
Konstantinos Balaskas, Florian Klemme, Georgios Zervakis +3
One of the major barriers that CMOS devices face at nanometer scale is increasing parameter variation due to manufacturing imperfections. Process variations severely inhibit the re…
TransAxx: Efficient Transformers with Approximate Computing
Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris +1
Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convoluti…
Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs
Florentia Afentaki, Michael Hefenbrock, Georgios Zervakis +1
Printed Electronics (PE) stands out as a promisingtechnology for widespread computing due to its distinct attributes, such as low costs and flexible manufacturing. Unlike tradition…
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…