FLAME: Fully Leveraging MoE Sparsity for Transformer on FPGA

MoE (Mixture-of-Experts) mechanism has been widely adopted in transformer-based models to facilitate further expansion of model parameter size and enhance generalization capabilities. However, the practical deployment of MoE mechanism for transformer on resource-constrained platforms, such as FPGA, remains challenging due to heavy memory footprints and impractical runtime costs introduced by the MoE mechanism. Diving into the MoE mechanism, we raise two key observations: (1) Expert weights are heavy but cold, making it ideal to leverage expert weight sparsity. (2) There exists highly skewed expert activation paths for MoE layers in transformer-based models, making it feasible to conduct expert prediction and prefetching. Motivated by these two observations, we propose FLAME, the first algorithm-hardware co-optimized MoE accelerating framework designed to fully leverage MoE sparsity for efficient transformer deployment on FPGA. First, to leverage expert weight sparsity, we integrate an N:M pruning algorithm, allowing for the pruning of expert weights without significantly compromising model accuracy. Second, to settle expert activation sparsity, we propose a circular expert prediction (CEPR) strategy. CEPR prefetches expert weights from external storage to on-chip cache before the activated expert index is determined. Last, we co-optimize both MoE sparsity through the introduction of an efficient pruning-aware expert buffering (PA-BUF) mechanism. Experimental results demonstrate that FLAME achieves 84.4% accuracy of expert prediction with merely two expert caches on-chip. In comparison with CPU and GPU, FLAME achieves 4.12× and 1.49× speedup, respectively.