14 citations · 40 across the 11 of their papers we have counts for
12 papers
Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics
Robert L. Z. Hoye, Markus Hellenbrand, Nuno Estrócio +17
The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and wa…
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…
Analog Weight Update Rule in Ferroelectric Hafnia, using pico-Joule Programming Pulses
Alexandre Baigol, Nikhil Garg, Matteo Mazza +5
In an effort to compete with the brain's efficiency at processing information, neuromorphic hardware combines artificial synapses and neurons using mixed-signal circuits and emergi…
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares +11
The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from brain-inspired…
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.…
Electroforming Kinetics in HfOx/Ti RRAM: Mechanisms Behind Compositional and Thermal Engineering
Manasa Kaniselvan, Kevin Portner, Donato Francesco Falcone +6
A critical issue affecting filamentary resistive random access memory (RRAM) cells is the requirement of high voltages during electroforming. Reducing the magnitude of these voltag…
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…
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition
Victoria Clerico, Anirvan Dutta, Donato Francesco Falcone +8
Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency.…
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…
A Back-End-Of-Line Compatible, Ferroelectric Analog Non-Volatile Memory
Laura Bégon-Lours, Mattia Halter, Diana Dávila Pineda +6
A Ferroelectric Analog Non-Volatile Memory based on a WOx electrode and ferroelectric HfZrO4 layer is fabricated at a low thermal budget (~375C), enabling BEOL processes and CMOS i…