4 citations · 9 across the 10 of their papers we have counts for
14 papers
Quantization and Deployment of Deep Neural Networks on Microcontrollers
Pierre-Emmanuel Novac, Ghouthi Boukli Hacene, Alain Pegatoquet +2
Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently…
DNN Quantization with Attention
Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich +1
Low-bit quantization of network weights and activations can drastically reduce the memory footprint, complexity, energy consumption and latency of Deep Neural Networks (DNNs). Howe…
Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Anush Sankaran, Olivier Mastropietro, Ehsan Saboori +4
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive acc…
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Vincent Gripon, Carlos Lassance, Ghouthi Boukli Hacene
Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years. Indeed, Deep Neural Networks are now the golden standard…
ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
Guillaume Coiffier, Ghouthi Boukli Hacene, Vincent Gripon
Typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through down…
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Miloš Nikolić, Ghouthi Boukli Hacene, Ciaran Bannon +5
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware…