42 citations · 144 across the 15 of their papers we have counts for
8 papers · 1 filter
Concurrent Self-testing of Neural Networks Using Uncertainty Fingerprint
Soyed Tuhin Ahmed, Mehdi B. tahoori
Neural networks (NNs) are increasingly used in always-on safety-critical applications deployed on hardware accelerators (NN-HAs) employing various memory technologies. Reliable con…
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…
Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock +3
Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. Bayesian Neural Netwo…
Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation
Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock +3
Recently, machine learning systems have gained prominence in real-time, critical decision-making domains, such as autonomous driving and industrial automation. Their implementation…
One-Shot Online Testing of Deep Neural Networks Based on Distribution Shift Detection
Soyed Tuhin Ahmed, Mehdi B. Tahoori
Neural networks (NNs) are capable of learning complex patterns and relationships in data to make predictions with high accuracy, making them useful for various tasks. However, NNs…
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…
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…