A Partial Reconfiguration-based scheme to mitigate Multiple-Bit Upsets for FPGAs in low-cost space applications. - Charlotte Frenkel; Jean-Didier Legat; David Bol (ReCoSoC, 2015)

Comparative analysis of redundancy schemes for soft-error detection in low-cost space applications. - Charlotte Frenkel; Jean-Didier Legat; David Bol (VLSI-SoC, 2016)

SleepTalker: A 28nm FDSOI ULV 802.15.4a IR-UWB transmitter SoC achieving 14pJ/bit at 27Mb/s with adaptive-FBB-based channel selection and programmable pulse shape. - Guerric de Streel; François Stas; Thibaut Gurne; François Durant; Charlotte Frenkel; David Bol (VLSI Circuits, 2016)

A compact phenomenological digital neuron implementing the 20 Izhikevich behaviors. - Charlotte Frenkel; Jean-Didier Legat; David Bol (BioCAS, 2017)

A fully-synthesized 20-gate digital spike-based synapse with embedded online learning. - Charlotte Frenkel; Giacomo Indiveri; Jean-Didier Legat; David Bol (ISCAS, 2017)

SleepTalker: A ULV 802.15.4a IR-UWB Transmitter SoC in 28-nm FDSOI Achieving 14 pJ/b at 27 Mb/s With Channel Selection Based on Adaptive FBB and Digitally Programmable Pulse Shaping. - Guerric de Streel; François Stas; Thibaut Gurne; François Durant; Charlotte Frenkel; Andreia Cathelin; David Bol (IEEE J. Solid State Circuits, 2017)

A 0.086-mm2 9.8-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS. - Charlotte Frenkel; Jean-Didier Legat; David Bol (CoRR, 2018)

A 65-nm 738k-Synapse/mm2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning. - Charlotte Frenkel; Jean-Didier Legat; David Bol (ISCAS, 2019)

A 40-to-80MHz Sub-4μW/MHz ULV Cortex-M0 MCU SoC in 28nm FDSOI With Dual-Loop Adaptive Back-Bias Generator for 20μs Wake-Up From Deep Fully Retentive Sleep Mode. - David Bol; Maxime Schramme; Ludovic Moreau; Thomas Haine; Pengcheng Xu 002; Charlotte Frenkel; Remi Dekimpe; François Stas; Denis Flandre (ISSCC, 2019)

Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training. - Charlotte Frenkel; Martin Lefebvre 002; David Bol (CoRR, 2019)

MorphIC: A 65-nm 738k-Synapse/mm2 Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning. - Charlotte Frenkel; Jean-Didier Legat; David Bol (IEEE Trans. Biomed. Circuits Syst., 2019)

A 0.086-mm2 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS. - Charlotte Frenkel; Martin Lefebvre 002; Jean-Didier Legat; David Bol (IEEE Trans. Biomed. Circuits Syst., 2019)

A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas. - Charlotte Frenkel; Jean-Didier Legat; David Bol (ISCAS, 2020)

Bottom-up and top-down neuromorphic processor design: Unveiling roads to embedded cognition. - Charlotte Frenkel (2020)

PCM-Trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials. - Yigit Demirag; Filippo Moro; Thomas Dalgaty; Gabriele Navarro; Charlotte Frenkel; Giacomo Indiveri; Elisa Vianello; Melika Payvand (ISCAS, 2021)

Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence. - Charlotte Frenkel; David Bol; Giacomo Indiveri (CoRR, 2021)

Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses. - Yigit Demirag; Charlotte Frenkel; Melika Payvand; Giacomo Indiveri (CoRR, 2021)

SleepRunner: A 28-nm FDSOI ULP Cortex-M0 MCU With ULL SRAM and UFBR PVT Compensation for 2.6-3.6-μW/DMIPS 40-80-MHz Active Mode and 131-nW/kB Fully Retentive Deep-Sleep Mode. - David Bol; Maxime Schramme; Ludovic Moreau; Pengcheng Xu 002; Rémi Dekimpe; Roghayeh Saeidi; Thomas Haine; Charlotte Frenkel; Denis Flandre (IEEE J. Solid State Circuits, 2021)

Sparsity provides a competitive advantage. - Charlotte Frenkel (Nat. Mach. Intell., 2021)

Biologically-inspired training of spiking recurrent neural networks with neuromorphic hardware. - Thomas Bohnstingl; Anja Surina; Maxime Fabre; Yigit Demirag; Charlotte Frenkel; Melika Payvand; Giacomo Indiveri; Angeliki Pantazi (AICAS, 2022)

Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions. - Arindam Basu; Lei Deng 003; Charlotte Frenkel; Xueyong Zhang (CICC, 2022)

Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems. - Matteo Cartiglia; Arianna Rubino; Shyam Narayanan; Charlotte Frenkel; Germain Haessig; Giacomo Indiveri; Melika Payvand (ISCAS, 2022)

A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing Ferroelectric Devices. - Shyam Narayanan; Erika Covi; Viktor Havel; Charlotte Frenkel; Suzanne Lancaster; Quang T. Duong; Stefan Slesazeck; Thomas Mikolajick; Melika Payvand; Giacomo Indiveri (ISCAS, 2022)

ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales. - Charlotte Frenkel; Giacomo Indiveri (ISSCC, 2022)

THOR - A Neuromorphic Processor with 7.29G TSOP$^2$/mm$^2$Js Energy-Throughput Efficiency. - Mayank Senapati; Manil Dev Gomony; Sherif Eissa; Charlotte Frenkel; Henk Corporaal (CoRR, 2022)

Online Spatio-Temporal Learning with Target Projection. - Thomas Ortner; Lorenzo Pes; Joris Gentinetta; Charlotte Frenkel; Angeliki Pantazi (AICAS, 2023)

SPAIC: A sub-μW/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders. - Shyam Narayanan; Matteo Cartiglia; Arianna Rubino; Charles Lego; Charlotte Frenkel; Giacomo Indiveri (BioCAS, 2023)

Event-based Classification with Recurrent Spiking Neural Networks on Low-end Micro-Controller Units. - Chiara Boretti; Luciano Prono; Charlotte Frenkel; Giacomo Indiveri; Fabio Pareschi; Mauro Mangia; Riccardo Rovatti; Gianluca Setti (ISCAS, 2023)

Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs. - Ali Safa; Jonah Van Assche; Charlotte Frenkel; André Bourdoux; Francky Catthoor; Georges G. E. Gielen (NICE, 2023)

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking. - Jason Yik; Soikat Hasan Ahmed; Zergham Ahmed; Brian Anderson; Andreas G. Andreou; Chiara Bartolozzi; Arindam Basu; Douwe den Blanken; Petrut Bogdan; Sander M. Bohté; Younes Bouhadjar; Sonia M. Buckley; et al. (CoRR, 2023)

Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence. - Charlotte Frenkel; David Bol; Giacomo Indiveri (Proc. IEEE, 2023)

Editorial: Focus issue on energy-efficient neuromorphic devices, systems and algorithms. - Adnan Mehonic; Charlotte Frenkel; Eleni Vasilaki (Neuromorph. Comput. Eng., 2023)

Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks. - Lorenzo Pes; Rick Luiken; Federico Corradi; Charlotte Frenkel (AICAS, 2024)

Grand Challenge on Neural Decoding for Motor Control of non-Human Primates. - Biyan Zhou; Pao-Sheng Vincent Sun; Jason Yik; Charlotte Frenkel; Vijay Janapa Reddi; Arindam Basu (BioCAS, 2024)

EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision. - Yufeng Yang; Adrian Kneip; Charlotte Frenkel (CoRR, 2024)

Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator. - Alejandro Linares-Barranco; Luciano Prono; Robert Legenstein; Giacomo Indiveri; Charlotte Frenkel (CoRR, 2024)

Introduction to the Special Issue on tinyML. - Theocharis Theocharides; Charlotte Frenkel; Lukas Cavigelli (ACM Trans. Embed. Comput. Syst., 2024)

An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Weight/Output Stationarity. - Nicolas Chauvaux; Adrian Kneip; Christoph Posch; Kofi Makinwa; Charlotte Frenkel (ISCAS, 2025)

TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks. - Marco Paul E. Apolinario; Kaushik Roy 001; Charlotte Frenkel (CoRR, 2025)

FREYA: A 0.023-mm²/Channel, 20.8- μW/Channel, Event-Driven 8-Channel SoC for Spiking End-to-End Sensing of Time-Sparse Biosignals. - Jonah Van Assche; Charlotte Frenkel; Ali Safa; Georges G. E. Gielen (IEEE Trans. Circuits Syst. I Regul. Pap., 2025)

Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data. - Douwe den Blanken; Charlotte Frenkel (CoRR, 2025)

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training. - Johannes von Oswald; Nino Scherrer; Seijin Kobayashi; Luca Versari; Songlin Yang; Maximilian Schlegel; Kaitlin Maile; Yanick Schimpf; Oliver Sieberling; Alexander Meulemans; Rif A. Saurous; Guillaume Lajoie; et al. (CoRR, 2025)