CMOS differential neural amplifier with high input impedance. - François Rummens; Sylvie Renaud; Noëlle Lewis (NEWCAS, 2015)

Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection. - François Rummens; Stéphane Ygorra; Hol C. Mayiss Boussamba; Sylvie Renaud; Noëlle Lewis (Int. J. Circuit Theory Appl., 2016)

Spiking Neural Networks Hardware Implementations and Challenges: A Survey. - Maxence Bouvier; Alexandre Valentian; Thomas Mesquida; François Rummens; Marina Reyboz; Elisa Vianello; Edith Beigné (ACM J. Emerg. Technol. Comput. Syst., 2019)

32.3 Electromagnetic Mechanical Energy-Harvester IC with No Off-Chip Component and One Switching Period MPPT Achieving up to 95.9% End-to-End Efficiency and 460% Energy-Extraction Gain. - Anthony Quelen; Gaël Pillonnet; Pierre Gasnier; François Rummens; Sebastien Boisseau (ISSCC, 2020)

1S1R sub-threshold operation in Crossbar arrays for low power BNN inference computing. - Joel Minguet Lopez; François Rummens; Lucas Reganaz; A. Heraud; Tifenn Hirtzlin; Laurent Grenouillet; Gabriele Navarro; Mathieu Bernard; Catherine Carabasse; Niccolo Castellani; Valentina Meli; S. Martin; et al. (IMW, 2022)

Scaling-up Memristor Monte Carlo with magnetic domain-wall physics. - Thomas Dalgaty; Shogo Yamada; Anca Molnos; Eiji Kawasaki; Thomas Mesquida; François Rummens; Tatsuo Shibata; Yukihiro Urakawa; Yukio Terasaki; Tomoyuki Sasaki; Marc Duranton (CoRR, 2023)

Compressed vector-matrix multiplication for Memristor-based ensemble neural networks. - Phan Anh Vu; François Rummens; Marielle Malfante; Bertrand Rivet; Thomas Dalgaty (ICRC, 2024)