5 citations · 8 across the 5 of their papers we have counts for
5 papers
Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat +2
Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions, making them a suitable choice in safety-critical applications. Additionally, their realization…
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat +2
Internet of Things (IoT) and smart wearable devices for personalized healthcare will require storing and computing ever-increasing amounts of data. The key requirements for these d…
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
Soyed Tuhin Ahmed, Michael Hefenbrock, Guillaume Prenat +2
Bayesian Neural Networks (BayNNs) can inherently estimate predictive uncertainty, facilitating informed decision-making. Dropout-based BayNNs are increasingly implemented in spintr…
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…