8 citations · 19 across the 19 of their papers we have counts for
20 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…
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…
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…
FLARE: Fault Attack Leveraging Address Reconfiguration Exploits in Multi-Tenant FPGAs
Jayeeta Chaudhuri, Hassan Nassar, Dennis R. E. Gnad +3
Modern FPGAs are increasingly supporting multi-tenancy to enable dynamic reconfiguration of user modules. While multi-tenant FPGAs improve utilization and flexibility, this paradig…
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…
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…
Hacking the Fabric: Targeting Partial Reconfiguration for Fault Injection in FPGA Fabrics
Jayeeta Chaudhuri, Hassan Nassar, Dennis R. E. Gnad +3
FPGAs are now ubiquitous in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. Major cloud service providers such as Amazon and Mi…
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…
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…
Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural Networks Using One Bayesian Test Vector
Soyed Tuhin Ahmed, Mehdi Tahoori
The performance of deep learning algorithms such as neural networks (NNs) has increased tremendously recently, and they can achieve state-of-the-art performance in many domains. Ho…
Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights
Soyed Tuhin Ahmed, Michael Hefenbrock, Mehdi B. Tahoori
The applications of artificial intelligence (AI) are rapidly evolving, and they are also commonly used in safety-critical domains, such as autonomous driving and medical diagnosis,…
MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs
Lilas Alrahis, Hassan Nassar, Jonas Krautter +4
The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configuration…
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…
Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat +2
Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions, making them a suitable choice in safety-critical applications. Additionally, their realization…
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat +2
Internet of Things (IoT) and smart wearable devices for personalized healthcare will require storing and computing ever-increasing amounts of data. The key requirements for these d…
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
Soyed Tuhin Ahmed, Michael Hefenbrock, Guillaume Prenat +2
Bayesian Neural Networks (BayNNs) can inherently estimate predictive uncertainty, facilitating informed decision-making. Dropout-based BayNNs are increasingly implemented in spintr…