LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908501792522240 |
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| author | Chang, Kaiyan Zhu, Wenlong Liang, Shengwen Li, Huawei Wang, Ying |
| author_facet | Chang, Kaiyan Zhu, Wenlong Liang, Shengwen Li, Huawei Wang, Ying |
| contents | Accurate and fast performance prediction for dataflow-based accelerators is vital for efficient hardware design and design space exploration, yet existing methods struggle to generalize across architectures, applications, and input-dependent control flows. We present LLMulator, a progressive numeric modeling framework leveraging the program semantic knowledge of pre-trained large language models (LLMs) for robust, hardware- and application-aware prediction. Our numeric model treats performance values as categorical token sequences, enabling range-agnostic estimates and confidence-aware predictions for unseen applications. To handle input-dependent control flows, we introduce a reinforcement learning-based dynamic calibration method, reducing cycle prediction error by 9.7% over static models and converging to 11.2% error after a few iterations. For cross-hardware generalization, we develop a progressive data augmentation strategy that generates diverse datasets covering multi-level dataflow structures, memory parameters, and loop mapping primitives, significantly boosting prediction accuracy across architectures and configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17826 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow Chang, Kaiyan Zhu, Wenlong Liang, Shengwen Li, Huawei Wang, Ying Hardware Architecture Accurate and fast performance prediction for dataflow-based accelerators is vital for efficient hardware design and design space exploration, yet existing methods struggle to generalize across architectures, applications, and input-dependent control flows. We present LLMulator, a progressive numeric modeling framework leveraging the program semantic knowledge of pre-trained large language models (LLMs) for robust, hardware- and application-aware prediction. Our numeric model treats performance values as categorical token sequences, enabling range-agnostic estimates and confidence-aware predictions for unseen applications. To handle input-dependent control flows, we introduce a reinforcement learning-based dynamic calibration method, reducing cycle prediction error by 9.7% over static models and converging to 11.2% error after a few iterations. For cross-hardware generalization, we develop a progressive data augmentation strategy that generates diverse datasets covering multi-level dataflow structures, memory parameters, and loop mapping primitives, significantly boosting prediction accuracy across architectures and configurations. |
| title | LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2508.17826 |