PerfReasoning: How Well Do LLMs Reason on Hardware Performance?
Read the original on arXiv AI →PerfReasoning is a new benchmark that tests large language models (LLMs) on their ability to reason about hardware performance and generate analytical performance‑model code. The benchmark presents workloads, architectures, and mapping specifications, asking models to compare mappings and predict off‑chip traffic and buffer requirements. While the best closed‑source models achieve over 90% accuracy on reasoning‑based Q&A and the top open‑weight model scores 82.4%, constructing full performance models remains difficult, with most models scoring below 15% and significant variability across runs. Task‑specific reinforcement learning can improve a 4B model’s mapping‑reasoning accuracy by 15.7 points, but feedback‑free self‑revision prompting is not reliably effective.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.