  The advancement of deep learning has led to the emergence of
Mixture-of-Experts (MoEs) models, known for their dynamic allocation of
computational resources based on input. Despite their promise, MoEs face
challenges, particularly in terms of memory requirements. To address this, our
work introduces SEER-MoE, a novel two-stage framework for reducing both the
memory footprint and compute requirements of pre-trained MoE models. The first
stage involves pruning the total number of experts using a heavy-hitters
counting guidance, while the second stage employs a regularization-based
fine-tuning strategy to recover accuracy loss and reduce the number of
activated experts during inference. Our empirical studies demonstrate the
effectiveness of our method, resulting in a sparse MoEs model optimized for
inference efficiency with minimal accuracy trade-offs.
