56 citations · 110 across the 25 of their papers we have counts for
30 papers
Length-Independent State Tracking Under a Parallel Scan
Julien Brandoit, Arthur Fyon, Thomas Braipson +5
Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear attention, and state space models en…
Grasping by interconnection: robust closing motions from coarse object templates
Julien Vanderheyden, Guillaume Drion, Fulvio Forni +1
Dexterous robot hands must often grasp objects whose shape, size, and pose are known only approximately. Grasp planners typically require accurate object models or correct errors w…
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
Julien Brandoit, Arthur Fyon, Damien Ernst +1
Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challengi…
Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA
Pascal Harmeling, Florent De Geeter, Guillaume Drion
Spiking Neural Networks (SNNs) can reduce energy consumption compared to conventional Artificial Neural Networks (ANNs) when spiking activity is sparse and the neuron model is hard…
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
Arthur Fyon, Julien Brandoit, Loris Mendolia +3
Always-on AI applications, from environmental sensors to biomedical implants, require ultra-low power consumption. Analog circuits offer a path to sub-microwatt inference, yet exis…
A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology
Arthur Fyon, Loris Mendolia, Jean-Michel Redouté +2
This work introduces a fully tunable, ultra-low power unipolar memory cell inspired by the Schmitt-trigger comparator and designed in CMOS using only nine transistors. The proposed…