1 citations · 1 across the 4 of their papers we have counts for
4 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…
A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses
Joao Henrique Quintino Palhares, Yann Beilliard, Jury Sandrini +7
In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current atten…