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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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Showing 2023Show all

6 papers · 1 filter

cs.LG2023★ 34 cited

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…

cs.LG2023★ 4 cited

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…

cs.AR2023★ 11 cited

Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons

Florentia Afentaki, Gurol Saglam, Argyris Kokkinis +3

Printed Electronics (PE) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing. This is particularly relevan…

cs.LG2023★ 12 cited

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…

cs.LG2023★ 18 cited

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…

cs.AR2023

Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons

Argyris Kokkinis, Georgios Zervakis, Kostas Siozios +2

The demand of many application domains for flexibility, stretchability, and porosity cannot be typically met by the silicon VLSI technologies. Printed Electronics (PE) has been int…