3 papers
cs.LG2025
Assessing the Performance of Analog Training for Transfer Learning
Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch +3
Analog in-memory computing is a next-generation computing paradigm that promises fast, parallel, and energy-efficient deep learning training and transfer learning (TL). However, ac…
cs.ET2024
The Inherent Adversarial Robustness of Analog In-Memory Computing
Corey Lammie, Julian Büchel, Athanasios Vasilopoulos +2
A key challenge for Deep Neural Network (DNN) algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on Anal…
cs.LG2024
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…