Pipeline Gradient-Based Model Training on Analog In-Memory Computing (AIMC) Accelerators: A Convergence and Efficiency Analysis

Pipeline parallelism, particularly the novel asynchronous approach, significantly accelerates the training of large deep neural networks on Analog In-Memory Computing (AIMC) accelerators, despite challenges posed by noisy gradients and update asymmetry inherent to analog hardware.