492 citations · 492 across the 9 of their papers we have counts for
4 papers · 1 filter
Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters
Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe +2
Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, scaling AIMC to train modern d…
Optimization under Persistent State-Dependent Bias: Gradient-based Method and Complexity Analysis
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +1
This paper studies the convergence of stochastic gradient descent (SGD) when the implemented updates are subject to a persistent and state-dependent bias, in which the desired upda…
Hybrid Digital-Analog Approximate Inverse Preconditioning for Krylov Methods
Shikhar Shah, Rui Peng Li, Tayfun Gokmen +3
Analog in-memory computing enables highly parallel matrix-vector multiplications with reduced data movement, but the resulting operations are noisy, quantized, and affected by devi…
Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
Quan Xiao, Jindan Li, Zhaoxian Wu +2
Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale large vision and language models. H…