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cs.LG2026
PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression
Shanfeng Huang, Rongqian Zhang, Zhou Fang +2
Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses severe memory challenges for de…
cs.LG2025
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…