137 citations · 253 across the 12 of their papers we have counts for
12 papers
Hybrid unary-binary design for multiplier-less printed Machine Learning classifiers
Giorgos Armeniakos, Theodoros Mantzakidis, Dimitrios Soudris
Printed Electronics (PE) provide a flexible, cost-efficient alternative to silicon for implementing machine learning (ML) circuits, but their large feature sizes limit classifier c…
MaRVIn: A Cross-Layer Mixed-Precision RISC-V Framework for DNN Inference, from ISA Extension to Hardware Acceleration
Giorgos Armeniakos, Alexis Maras, Sotirios Xydis +1
The evolution of quantization and mixed-precision techniques has unlocked new possibilities for enhancing the speed and energy efficiency of NNs. Several recent studies indicate th…
A Bespoke Design Approach to Low-Power Printed Microprocessors for Machine Learning Applications
Panagiotis Chaidos, Giorgos Armeniakos, Sotirios Xydis +1
Printed electronics have gained significant traction in recent years, presenting a viable path to integrating computing into everyday items, from disposable products to low-cost he…
Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
Giorgos Armeniakos, Georgios Mentzos, Dimitrios Soudris
The rapid growth of microcontroller-based IoT devices has opened up numerous applications, from smart manufacturing to personalized healthcare. Despite the widespread adoption of e…
Mixed-precision Neural Networks on RISC-V Cores: ISA extensions for Multi-Pumped Soft SIMD Operations
Giorgos Armeniakos, Alexis Maras, Sotirios Xydis +1
Recent advancements in quantization and mixed-precision approaches offers substantial opportunities to improve the speed and energy efficiency of Neural Networks (NN). Research has…
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…
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…
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…
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…
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +1
Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond…
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…