FORGE: Procedural Weight Generation for Sparse MoE Inference
We introduce FORGE (Fast On-chip Rotor Generation for Experts), a procedural weight generation system for Sparse Mixture-of-Experts (MoE) inference. Rather than storing and loading every expert weight tensor from HBM, we show how a compact accelerator inside each Streaming Multiprocessor can regenerate them on-the-fly from $O(\log^2 d)$ rotor parameters per projection. Per replaced layer, gigabytes of stored expert weights are approximated with under a megabyte of seeds. This addresses the dual memory bottleneck of autoregressive Sparse MoE decode, where per-expert FFN operations are HBM-bandwidth-bound at small routed-token counts and the aggregate expert footprint forces multi-node Expe...
| Authors | Chanyoung Moon, Travis Pence, Daisuke Yamada et al. |
|---|---|
| Primary category | cs.AR |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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