3 papers
cs.LG2026
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…
cs.LG2026
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
Zikun Liu, Liang Luo, Qianru Li +31
Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance, their computational demands…
cs.IR2024
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
Fang Zhou, Yaning Huang, Dong Liang +21
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and s…