8 papers
Axon: A Synthesizing Superoptimizer for Tensor Programs
Akash Kothari, Shaowei Zhu, Daniel Kroening +1
Writing high performance kernels for AI accelerators requires deep expertise in tiling, instruction selection, data layout, and operator fusion placing a significant burden on prog…
MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation
Jason Yoo, Rajarshi Saha, Shaowei Zhu +3
Despite rapid progress in LLM-based code generation, writing correct and performant kernels for hardware accelerators remains a key bottleneck in scaling modern ML workloads. We pr…
Ekka: Automated Diagnosis of Silent Errors in LLM Inference
Yile Gu, Zhen Zhang, Shaowei Zhu +4
LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introduce silent errors where output…
AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
Genghan Zhang, Shaowei Zhu, Anjiang Wei +6
We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-pro…
A Categorical Basis for Robust Program Analysis
Zachary Kincaid, Shaowei Zhu
Users of program analyses expect that results change predictably in response to changes in their programs, but many analyses fail to provide such robustness. This paper introduces…
TritonRL: Training LLMs to Think and Code Triton Without Cheating
Jiin Woo, Shaowei Zhu, Allen Nie +3
The rapid evolution of Large Language Models (LLMs) has driven a growing demand for automated, high-performance system kernels to accelerate machine learning workloads. We introduc…