6 citations · 18 across the 24 of their papers we have counts for
11 papers · 1 filter
NOSA: Native and Offloadable Sparse Attention
Yuxiang Huang, Pengjie Wang, Jicheng Han +9
Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-free KV cache offloa…
StateX: Enhancing RNN Recall via Post-training State Expansion
Xingyu Shen, Yingfa Chen, Zhen Leng Thai +3
Recurrent neural networks (RNNs), such as linear attention and state-space models, have gained popularity due to their constant per-token complexity when processing long contexts.…
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity
Chenyang Song, Weilin Zhao, Xu Han +5
To alleviate the computational burden of large language models (LLMs), architectures with activation sparsity, represented by mixture-of-experts (MoE), have attracted increasing at…
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Shangzhan Li, Zefan Wang, Ye He +8
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations thro…
A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
Xiaoang Xu, Shuo Wang, Xu Han +6
Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing…
AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
Xuanle Zhao, Zilin Sang, Yuxuan Li +7
Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-…
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
Weilin Zhao, Tengyu Pan, Xu Han +9
Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify…
Cost-Optimal Grouped-Query Attention for Long-Context Modeling
Yingfa Chen, Yutong Wu, Chenyang Song +5
Grouped-Query Attention (GQA) is a widely adopted strategy for reducing the computational cost of attention layers in large language models (LLMs). However, current GQA configurati…
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Jianling Li, Shangzhan Li, Zhenye Gao +9
Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and access…
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs
Yuxiang Huang, Mingye Li, Xu Han +7
While long-context inference is crucial for advancing large language model (LLM) applications, its prefill speed remains a significant bottleneck. Current approaches, including seq…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…