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20222025
most citedHardware Approximate Techniques for Deep Neural Network Accelerators: A Survey

137 citations · 253 across the 12 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

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★ 15 cited

Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos +4

The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computin…

cs.AR2023★ 29 cited

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos +4

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications invo…

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…