English(EN) HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning

新AI框架HLS-Seek优化硬件设计生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了HLS-Seek，一个用于从C/C++代码生成硬件设计的新颖框架，该框架优先考虑结果质量（QoR），如延迟和资源利用率。该系统利用带有比较代理奖励模型的强化学习，避免了在循环中进行完全综合的需要，并在预测帕累托最优设计方面取得了高精度。HLS-Seek还包含一个感知不确定性的蒙特卡洛Dropout切换机制，以使用真实的综合数据来改进其代理模型，与现有方法相比，从而提高了性能并加快了训练速度。 AI

影响该框架可以通过提高AI驱动的综合代码生成的效率和准确性来加速硬件设计。

排序理由详细介绍用于硬件设计的新AI框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Qingyun Zou, Feng Yu, Hongshi Tan, Yao Chen, Bingsheng He, WengFai Wong · 2026-09-07 04:00

HLS-Seek：通过代理比较奖励强化学习实现面向质量感知的高层次综合代码生成

arXiv:2605.13536v2 Announce Type: replace-cross Abstract: High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR)---latency and resource utilization---critically governed by pragma configurations and code structure. Existing…

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报道来源 [1]

HLS-Seek：通过代理比较奖励强化学习实现面向质量感知的高层次综合代码生成

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