112 citations · 208 across the 9 of their papers we have counts for
3 papers · 1 filter
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.…
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, CMOS compatible ferroelectric Field Effect Transistor for synaptic weights
Mattia Halter, Laura Bégon-Lours, Valeria Bragaglia +5
Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck o…