10 papers
QALM: Escaping Local Minima via Interleaved Exploration and Exploitation in Quantum Circuit Optimization
Aidan Wagner, Mingkuan Xu, Pengyu Liu +2
Quantum circuit optimizers face a fundamental limitation in how they tolerate temporary cost increases. At one extreme, greedy rule-based optimizers immediately apply any cost-redu…
Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents
Zhuoming Chen, Xinrui Zhong, Qilong Feng +5
Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse at…
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Jingyuan Wang, Zhihao Jia, Chenyu Liu +7
EEG foundation models (EFMs) achieve strong cross-subject and cross-task generalization through large-scale pretraining and downstream fine-tuning. Through empirical analysis, we o…
Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel
Hongyi Jin, Bohan Hou, Guanjie Wang +18
Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter-kernel parallelism. Recent me…
Prism: Symbolic Superoptimization of Tensor Programs
Mengdi Wu, Xiaoyu Jiang, Oded Padon +1
This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large class…
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
Zhuoming Chen, Avner May, Ruslan Svirschevski +4
As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged…