arXiv (All)
open access
KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?
Modern AI systems depend on specialized accelerator kernels, whose development is complicated by increasingly diverse operators and hardware. LLMs and agentic systems promise to automate this work, but existing evaluations do not show whether their performance transfers across operator sources and h
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ThAME: 3D Memory-Enabled Heterogeneous Accelerator for LLM Mixture of Experts#386840
- Bridging the Deployment Gap: Integrating AI into Accelerator Control Systems#403519
- Blink: CPU-Free LLM Inference by Delegating the Serving Stack to GPU and SmartNIC#2544
- KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation#1117786
- KLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts#1141552
Record · ID 987764
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive