137 citations · 424 across the 38 of their papers we have counts for
13 papers · 1 filter
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…
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…
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: 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…
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…
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…
On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design
Giorgos Armeniakos, Paula L. Duarte, Priyanjana Pal +3
Printed electronics (PE) technology provides cost-effective hardware with unmet customization, due to their low non-recurring engineering and fabrication costs. PE exhibit features…
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be in…
Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that fea…
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +1
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test. To bypass high energy consumption issues, appro…
Cross-Layer Approximation For Printed Machine Learning Circuits
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed electronics (PE) feature low non-recurring engineering costs and low per unit-area fabrication costs, enabling thus extremely low-cost and on-demand hardware. Such low-cost…
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch
Dimitrios Danopoulos, Georgios Zervakis, Kostas Siozios +2
Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators. However, evaluating the accuracy of approximate DNNs is…
Control Variate Approximation for DNN Accelerators
Georgios Zervakis, Ourania Spantidi, Iraklis Anagnostopoulos +2
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Mont…