1 citations · 1 across the 2 of their papers we have counts for
5 papers
MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference
Romain Facq, Sami Ben Ali, Olivier Sentieys
Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full precision accuracy. However, apply…
A Divide-and-Conquer Strategy for Hard-Label Extraction of Deep Neural Networks via Side-Channel Attacks
Benoit Coqueret, Mathieu Carbone, Olivier Sentieys +1
During the past decade, Deep Neural Networks (DNNs) proved their value on a large variety of subjects. However despite their high value and public accessibility, the protection of…
AdaQAT: Adaptive Bit-Width Quantization-Aware Training
Cédric Gernigon, Silviu-Ioan Filip, Olivier Sentieys +2
Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. However, high computational complexity and energy costs of modern DNNs make…
A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training
Sami Ben Ali, Silviu-Ioan Filip, Olivier Sentieys
Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge b…
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence
Benoit Coqueret, Mathieu Carbone, Olivier Sentieys +1
Artificial intelligence, and specifically deep neural networks (DNNs), has rapidly emerged in the past decade as the standard for several tasks from specific advertising to object…