14 citations · 14 across the 4 of their papers we have counts for
4 papers
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…
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…
Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits
Nikhil Garg, Davide Florini, Patrick Dufour +8
Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron wi…