662 citations
- ETH ZurichCH43 papers
- IBM (United States)US15 papers
- École Polytechnique Fédérale de LausanneCH9 papers
- University of BaselCH8 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- Swiss Federal Laboratories for Materials Science and TechnologyCH7 papers
- IBM Research - Thomas J. Watson Research CenterUS6 papers
- University of ZurichCH6 papers
- Universidade de Santiago de CompostelaES5 papers
- Center for Research in Molecular Medicine and Chronic DiseasesES4 papers
- Chalmers University of TechnologySE4 papers
- Forschungszentrum JülichDE4 papers
7 papers · 1 filter
Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators
Wooseok Choi, Tommaso Stecconi, Donato Francesco Falcone +14
Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and trainin…
Study of Resistive Switching Dynamics and Memory States Equilibria in Analog Filamentary Conductive-Metal-Oxide/HfOx ReRAM via Compact Modeling
Matteo Galetta, Donato Francesco Falcone, Victoria Clerico +7
Resistive Random Access Memory (ReRAM) devices offer a promising solution for next-generation non-volatile memory and neuromorphic computing systems. Yet, existing compact models f…
Analytical Modelling of the Transport in Analog Filamentary Conductive-Metal-Oxide/HfOx ReRAM Devices
Donato Francesco Falcone, Stephan Menzel, Tommaso Stecconi +4
The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog fi…
All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi +9
Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or trainin…
The Inherent Adversarial Robustness of Analog In-Memory Computing
Corey Lammie, Julian Büchel, Athanasios Vasilopoulos +2
A key challenge for Deep Neural Network (DNN) algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on Anal…
Energy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing
Geethan Karunaratne, Manuel Le Gallo, Michael Hersche +4
The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks com…