Showing cs.LGShow all
2 papers · 1 filter
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.LG2024
Towards Exact Gradient-based Training on Analog In-memory Computing
Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch +1
Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While…