13 citations · 13 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
Energy-convergence trade off for the training of neural networks on bio-inspired hardware
Nikhil Garg, Paul Uriarte Vicandi, Yanming Zhang +5
The increasing deployment of wearable sensors and implantable devices is shifting AI processing demands to the extreme edge, necessitating ultra-low power for continuous operation.…
Hardware Implementation of Ring Oscillator Networks Coupled by BEOL Integrated ReRAM for Associative Memory Tasks
Wooseok Choi, Thomas van Bodegraven, Jelle Verest +8
We demonstrate the first hardware implementation of an oscillatory neural network (ONN) utilizing resistive memory (ReRAM) for coupling elements. A ReRAM crossbar array chip, integ…
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…