Omobayode Fagbohungbe — accepted AI-conference papers

2025

Analog Foundation Models

Julian Büchel, Iason Chalas, Giovanni Acampa, An Chen, Omobayode Fagbohungbe, Hsinyu Tsai +4

NeurIPS 2025poster

Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-based architectures. However, AIMC introduces fundamental challenges such as noisy computations and strict constraints on…

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2025

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

Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Omobayode Fagbohungbe, Tianyi Chen

NeurIPS 2025oral

As the economic and environmental costs of training and deploying large vision or language models increase dramatically, analog in-memory computing (AIMC) emerges as a promising energy-efficient solution. However, the training perspective, especially its training dynamic, is underexplored. In AIMC h…

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