From the 1 of 7 linked papers with an AI index.
7 papers
Optimization under Persistent State-Dependent Bias: Gradient-based Method and Complexity Analysis
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +1
The paper analyzes how stochastic gradient descent behaves when updates are consistently distorted by state-dependent scaling, shows this leads to a biased solution, and introduces…
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…
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…
On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +3
Aiming to accelerate the training of large deep neural networks (DNN) in an energy-efficient way, analog in-memory computing (AIMC) emerges as a solution with immense potential. AI…
Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +2
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 en…
In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learning
Jindan Li, Zhaoxian Wu, Gaowen Liu +2
Analog in-memory computing (AIMC) accelerators enable efficient deep neural network computation directly within memory using resistive crossbar arrays, where model parameters are r…