Post Training Sparsity

Post-training sparsity (PTS) focuses on making neural networks more efficient by removing unnecessary connections *after* the initial training is complete, aiming to reduce computational cost and memory footprint without significant performance loss. Current research emphasizes developing algorithms that efficiently determine the optimal sparsity pattern across different layers, addressing challenges like accuracy degradation at high sparsity levels and achieving fast convergence. This approach holds significant promise for deploying large models on resource-constrained devices and accelerating inference, impacting both the efficiency of machine learning research and its practical applications.