Hydra: Harnessing Expert Popularity for Efficient Mixture-of-Expert Inference on Chiplet System

The rapid growth of model sizes in advanced artificial intelligence algorithms, particularly in Transformerbased large language models (LLMs), has led to significant computational overhead. Mixture-of-Expert (MoE) models offer a solution through their sparsely gating mechanism but introduce new challenges of extensive all-to-all communication and model computational inefficiencies. This paper presents Hydra, a software/hardware co-design aimed at accelerating MoE inference on chiplet-based architectures. In software, Hydra employs a popularity-aware expert mapping strategy to optimize interchiplet communication. In hardware, it incorporates Content Addressable Memory (CAM) to eliminate expensive explicit token (un)-permutation based on sparse matrix multiplications and a redundant-calculation-skipping softmax engine to bypass unnecessary division and exponential operations. Evaluated in 22 nm technology, Hydra achieves latency reductions of $14.2 \times$ and $3.5 \times$ and power reductions of $169.1 \times$ and $18.9 \times$ over GPU and state-of-the-art MoE accelerator, respectively, thereby offering a scalable and efficient solution for MoE model deployment.