Paper
SimulatorCoder: DNN Accelerator Simulator Code Generation and Optimization via Large Language Models
Authors
Yuhuan Xia, Tun Li, Hongji Zhou, Xianfa Zhou, Chong Chen, Ruiyu Zhang
Abstract
This paper presents SimulatorCoder, an agent powered by large language models (LLMs), designed to generate and optimize deep neural network (DNN) accelerator simulators based on natural language descriptions. By integrating domain-specific prompt engineering including In-Context Learning (ICL), Chain-of-Thought (CoT) reasoning, and a multi-round feedback-verification flow, SimulatorCoder systematically transforms high-level functional requirements into efficient, executable, and architecture-aligned simulator code. Experiments based on the customized SCALE-Sim benchmark demonstrate that structured prompting and feedback mechanisms substantially improve both code generation accuracy and simulator performance. The resulting simulators not only maintain cycle-level fidelity with less than 1% error compared to manually implemented counterparts, but also consistently achieve lower simulation runtimes, highlighting the effectiveness of LLM-based methods in accelerating simulator development. Our code is available at https://github.com/xiayuhuan/SimulatorCoder.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.17169v1</id>\n <title>SimulatorCoder: DNN Accelerator Simulator Code Generation and Optimization via Large Language Models</title>\n <updated>2026-02-19T08:34:18Z</updated>\n <link href='https://arxiv.org/abs/2602.17169v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.17169v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>This paper presents SimulatorCoder, an agent powered by large language models (LLMs), designed to generate and optimize deep neural network (DNN) accelerator simulators based on natural language descriptions. By integrating domain-specific prompt engineering including In-Context Learning (ICL), Chain-of-Thought (CoT) reasoning, and a multi-round feedback-verification flow, SimulatorCoder systematically transforms high-level functional requirements into efficient, executable, and architecture-aligned simulator code. Experiments based on the customized SCALE-Sim benchmark demonstrate that structured prompting and feedback mechanisms substantially improve both code generation accuracy and simulator performance. The resulting simulators not only maintain cycle-level fidelity with less than 1% error compared to manually implemented counterparts, but also consistently achieve lower simulation runtimes, highlighting the effectiveness of LLM-based methods in accelerating simulator development. Our code is available at https://github.com/xiayuhuan/SimulatorCoder.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AR'/>\n <published>2026-02-19T08:34:18Z</published>\n <arxiv:primary_category term='cs.AR'/>\n <author>\n <name>Yuhuan Xia</name>\n </author>\n <author>\n <name>Tun Li</name>\n </author>\n <author>\n <name>Hongji Zhou</name>\n </author>\n <author>\n <name>Xianfa Zhou</name>\n </author>\n <author>\n <name>Chong Chen</name>\n </author>\n <author>\n <name>Ruiyu Zhang</name>\n </author>\n </entry>"
}