SOURCE-LINKED INTELLIGENCE
PerfReasoning: How Well Do LLMs Reason on Hardware Performance?
Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computation, data reuse, storage, and movement. We introduce PerfReasoning, a benchmark that evaluates LLMs both as direct performance reasoners and as generators of analytical performance-model code. Given workload, architecture, and mapping specifications, models compare mappings and predict off-chip traffic and buffer requirements. The strongest closed-source models exceed 90% on reasoning-based Q&A, and the best open-weight model reaches 82.4%. How
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T21:00:06.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.