Analytic variability study of inference accuracy in RRAM arrays with a binary tree winner-take-all circuit for neuromorphic applications. - Jonas Doevenspeck; Robin Degraeve; Stefan Cosemans; Philippe Roussel; Bram-Ernst Verhoef; Rudy Lauwereins; Wim Dehaene (ESSDERC, 2018)

Low Voltage Transient RESET Kinetic Modeling of OxRRAM for Neuromorphic Applications. - Jonas Doevenspeck; Robin Degraeve; Andrea Fantini; Peter Debacker; Diederik Verkest; Rudy Lauwereins; Wim Dehaene (IRPS, 2019)

A 22 nm, 1540 TOP/s/W, 12.1 TOP/s/mm2 in-Memory Analog Matrix-Vector-Multiplier for DNN Acceleration. - Ioannis A. Papistas; Stefan Cosemans; Bram Rooseleer; Jonas Doevenspeck; Myung Hee Na; Arindam Mallik; Peter Debacker; Diederik Verkest (CICC, 2021)

Noise tolerant ternary weight deep neural networks for analog in-memory inference. - Jonas Doevenspeck; Peter Vrancx; Nathan Laubeuf; Arindam Mallik; Peter Debacker; Diederik Verkest; Rudy Lauwereins; Wim Dehaene (IJCNN, 2021)

DIANA: An End-to-End Energy-Efficient Digital and ANAlog Hybrid Neural Network SoC. - Kodai Ueyoshi; Ioannis A. Papistas; Pouya Houshmand; Giuseppe Maria Sarda; Vikram Jain; Man Shi; Qilin Zheng; Juan Sebastian Piedrahita Giraldo; Peter Vrancx; Jonas Doevenspeck; Debjyoti Bhattacharjee; Stefan Cosemans; et al. (ISSCC, 2022)

Dynamic Quantization Range Control for Analog-in-Memory Neural Networks Acceleration. - Nathan Laubeuf; Jonas Doevenspeck; Ioannis A. Papistas; Michele Caselli; Stefan Cosemans; Peter Vrancx; Debjyoti Bhattacharjee; Arindam Mallik; Peter Debacker; Diederik Verkest; Francky Catthoor; Rudy Lauwereins (ACM Trans. Design Autom. Electr. Syst., 2022)

11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge. - Pascal Alexander Hager; Bert Moons; Stefan Cosemans; Ioannis A. Papistas; Bram Rooseleer; Jeroen Van Loon; Roel Uytterhoeven; Florian Zaruba; Spyridoula Koumousi; Milos Stanisavljevic; Stefan Mach; Sebastiaan Mutsaards; et al. (ISSCC, 2024)