14 citations · 32 across the 7 of their papers we have counts for
4 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…