1 citations · 2 across the 5 of their papers we have counts for
7 papers
Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference
Xingyu Xie, Xiaoyong Xie, Zhouchen Lin +1
Tensor parallelism ends every layer with an all-reduce that sits between two dependent matrix multiplications, so its cost cannot be hidden and grows as compute outpaces interconne…
Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations
Tong Zhang, Jiangning Zhang, Zhucun Xue +9
Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, e…
Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability
Xingyu Xie, Zhaochen Yu, Yue Liao +3
Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within…
(Dis)Proving Spectre Security with Speculation-Passing Style
Santiago Arranz-Olmos, Gilles Barthe, Lionel Blatter +2
Constant-time (CT) verification tools are commonly used for detecting potential side-channel vulnerabilities in cryptographic libraries. Recently, a new class of tools, called spec…
GRIFFIN: Effective Token Alignment for Faster Speculative Decoding
Shijing Hu, Jingyang Li, Xingyu Xie +3
Speculative decoding accelerates inference in large language models (LLMs) by generating multiple draft tokens simultaneously. However, existing methods often struggle with token m…
Optimization Hyper-parameter Laws for Large Language Models
Xingyu Xie, Kuangyu Ding, Shuicheng Yan +2
Large Language Models have driven significant AI advancements, yet their training is resource-intensive and highly sensitive to hyper-parameter selection. While scaling laws provid…
LoCo: Low-Bit Communication Adaptor for Large-scale Model Training
Xingyu Xie, Zhijie Lin, Kim-Chuan Toh +1
To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchroni…