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K. Sankaralingam

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Preprint Sep 2026

Rosetta: Automating First-Principles Performance Modeling Using Multi-Agent LLMs

Analytical performance models --- derivations of throughput or speedup from hardware parameters --- make claims independently verifiable and expose binding constraints, yet rarely accompany architecture papers because building one by hand takes weeks of expert effort. We present Rosetta, a multi-agent LLM pipeline that...

K. Sankaralingam · 0 citations
#artificial intelligence Preprint Sep 2026

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...

Da Zhao, K. Sankaralingam, Christos Kozyrakis et al. · 0 citations
Review Jul 2026

Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers?

Gauntlet, an open-source pipeline that analyzes a paper through five independent expert-persona reviewers and an adversarial synthesis stage is studied, and a 98-paper automated ablation shows the gain comes from the multi-agent structure.

N. Aggarwal, A. Dubal, Sreeraj Kannakarankodi et al. · 0 citations

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