The paper investigates whether locally deployed large language models can automate hardware design workflows that involve repetitive, dependency-ordered operations using specialized tools. A Model Context Protocol (MCP) server is created to emulate a proprietary hardware design tool, and a benchmark tests single and multi-step edits, invalid requests, misspelled prompts, and multi-server contexts. Seven open-source models are evaluated across different pipeline choices, revealing that strong models can nearly fully cover expected calls, but reliability hinges on task structure and agent configuration, with comprehensive tool descriptions reducing failures and multi-agent setups aiding weaker models at the cost of extra calls.
By Leonardo Liparulo, Francesco Pierri
arXiv:2608. 09874v1 Announce Type: new Abstract: Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture discovery remains challenging due to vast search spaces, strict hardware budgets, and long simulation times.
By Abraham Gonzalez, Raghav Gupta, Akanksha Jain, Hanna Alam, Alexander Novikov, Po-Sen Huang, Matej Balog, Marvin Eisenberger, Sergey Shirobokov, Ng\^an V\~u, Hank Levy, Borivoje Nikoli\'c, Sagar Karandikar, Martin Dixon, Parthasarathy Ranganathan
arXiv:2512. 18725v3 Announce Type: replace Abstract: Machine learning (ML) inference serving systems can schedule requests to improve GPU utilization and to meet service level objectives (SLOs) or deadlines.
By Haidong Zhao, Nikolaos Georgantas
arXiv:2608. 06183v1 Announce Type: new Abstract: Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making.
By Jia Xiong, Runkai Li, Chenxu Niu, Guangyuan Gao, Changwen Xing, Yifan Zhang, Xinlai Wan, Jieran Cui, Chen Bai, Yusheng Hua, Ying Wang, Ming Ling, Xi Wang, Tao Xie
arXiv:2607. 20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time.
By Bronislav Sidik, Chaya Levi, Nizzan Kimhi
arXiv:2606.20487v2 Announce Type: replace
Abstract: Computer use agents are expanding from single-device operation toward cross-device systems that coordinate tasks across heterogeneous environments....
By Shu Yao, Yuhua Luo, Qian Long, Jingru Fan, Yuheng Wang, Lin Wu, Zhuoyuan Yu, Yufan Dang, Huatao Li, Chen Qian
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
arXiv:2606. 25532v1 Announce Type: cross Abstract: Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatible designs.
By Jiangwei Zhang, Wen Sun, Chong Wang, Shiyao Li, Cheng Che, Chunjing Han, Dan Meng, Jian Yang, Yu Wang, Rui Hou
The paper introduces TraceDSE, an agentic design space exploration framework for jointly mapping AI inference workloads to heterogeneous edge SoCs and configuring each processing unit. Unlike traditional black-box optimization, TraceDSE uses a proposer‑critic loop powered by large language models and enriched with system execution traces to identify bottlenecks and refine design choices. Experiments on an Intel Meteor Lake SoC show that TraceDSE outperforms state‑of‑the‑art evolutionary and Bayesian methods, improving Pareto frontier hypervolume by up to 68% while reducing hardware evaluations by 6–9×.
By Geetha Prasuna Yarramneni, Surya Selvam, Wilfried Haensch, Anand Raghunathan
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:2606. 19390v1 Announce Type: cross Abstract: A protocol driven framework is presented that binds SBOM and AIBOM artefacts to deterministic environment capture and structured runtime telemetry.
By Petar Radanliev, Omar Santos, Carsten Maple, Kay Atefi
arXiv:2610.27797v1 Announce Type: new
Abstract: 6G will be designed faster than it can be built: AI-driven search already proposes physical-layer algorithms at machine speed, while carrying a single...
By Eric Mittag, Lukas Brechtel, Maximilian Stark, Hans D. Schotten