MaxKernel is a multi‑agent system designed to generate high‑performance custom kernels for TPUs. It offers three paradigms: a Human‑in‑the‑Loop agent for collaborative design, an Autonomous agent that runs a fully automated optimization loop, and a Graph‑Based Autonomous Search for global exploration. All paradigms share specialized sub‑agents for planning, implementation, debugging, testing, and profiling, and the system consistently matches expert hand‑tuned baselines on the JaxBench suite and real‑world workloads.
By Shangkun Wang, Nina Cai, Charles Hoong, Julian Walker, Gerson Kroiz, George Vanica, Deepak Patil, Andi Gavrilescu, Hassan Sipra, Sethu Sankaran
arXiv:2606. 04126v1 Announce Type: cross Abstract: We introduce HighTide, an evolving AI-assisted benchmark suite.
By Benjamin Goldblatt, Paolo Pedroso, Farhad Modaresi, Ethan Sifferman, Matthew R. Guthaus
Large Language Model agents are being explored for chip design, but most methods work directly at RTL. This study compares Direct RTL Design, Agent-based HLS Design, Post-Compiler HLS Refinement, and Post-HLS RTL Refinement, and proposes a combined workflow called Agent-based HLS with RTL Refinement (AHRR). Using FPGAs for evaluation, AHRR achieves a 2.6× geometric‑mean speedup over Direct RTL Design across an 11‑task benchmark suite, demonstrating that higher‑level abstractions and subsequent RTL refinement can improve chip design efficiency.
By Zijian Ding, Yang Zou, Yizhou Sun, Jason Cong
KernelGenBench is a unified benchmark that evaluates large language models and agentic systems for generating efficient Triton kernels across diverse operator sources and hardware platforms. It covers 210 operators from PyTorch ATen, vLLM, and cuBLAS, and tests a 110‑operator subset on six different chips, consuming over 15 billion tokens in evaluation. The study finds that no single method dominates across all sources and platforms, with significant variations in correctness and performance depending on the operator source and hardware, and that agentic approaches require millions of tokens per successful operator.
By Peiyu Zang, Jian Tao, Jialing Zhang, Yichen Yuan, Wentao Zhang, Guang Liu, Yonghua Lin
arXiv:2610.38329v1 Announce Type: cross
Abstract: Frontier large language models write correct CUDA kernels for most single-operator benchmark tasks, yet their accuracy collapses on accelerators whos...
By Byonggon Chun
arXiv:2607. 27231v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specialized and labor-intensive task.
By Peiyu Zang, Jian Tao, Jialing Zhang, Yichen Yuan, Wentao Zhang, Guang Liu, Yonghua Lin