Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions

NeurIPSOral2025

Authors
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Omobayode Fagbohungbe, Tianyi Chen
Affiliation
Cornell University
Venue
NeurIPS 2025
Track
Oral

TL;DR

Leveraging a residual learning framework to support the model training on non-ideal analog in-memory computing hardware…

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

memory rag

← All NeurIPS 2025 Oral papers · Browse the whole archive