cs.AR · 2026-08-03 · No. 73

Hardware Architecture, 2026-08-03.

3 new papers in cs.AR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    RTLCurator: Label-Efficient Data Curation for RTL Generation

    Siyang Cai, Cangyuan Li, Wenjing Chang, Kun Wang, Haoyu Gao, Yinhe Han, Ying Wang

    cs.AR · cs.LG

    Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized. Synthesis provides scale but not correctness, and in two widely used RTL datasets only 24.4% and 53.5% of pairs pass generated functional tests. This raises the question of how much of such a corpus to keep and which part of it....

    arxiv.org/abs/2607.29283 · PDF

  2. 02

    Open-Source LLM-Driven Formal Verification: A Multi-Agent Pipeline for RTL Repair

    Ha Trung Tran

    cs.AR · cs.LG · cs.SE

    Verification consumes the majority of modern chip design effort, yet the formal verification tools that provide mathematical guarantees of correctness remain expensive and restrictively licensed. While large language models (LLMs) have shown promise for hardware design, existing approaches to RTL repair validate their results through simulation - which exercises only a subset of inputs - or rely on commercial tools, and few combine formal...

    arxiv.org/abs/2607.28877 · PDF

  3. 03

    Characterizing LLM Kernel Access and Memory Interaction in Multi-Partition NUMA GPUs

    Donghyeon Joo, Sooraj Puthoor, Nuwan Jayasena, Bahar Asgari

    cs.AR · cs.DC · cs.PF

    Large language model (LLM) workloads motivate multi-partition GPUs as a path to scaling compute and memory capacity, but their non-uniform memory access characteristics and inter-partition communication can amplify contention and degrade locality, leading to suboptimal kernel latency. To address this, we analyze performance-critical LLM kernel implementations spanning weight projection, mixture-of-experts, and attention variants of...

    arxiv.org/abs/2607.28824 · PDF

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