AI时代硬件设计需人机协同，高阶综合仍是关键桥梁
From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis
- 用高阶综合作为人机协作的抽象层，支持快速迭代与可移植性
- 指出当前工具缺乏性能反馈、接口僵化、难调试等痛点
- 提出从协作者到自主伙伴的演化框架，适合系统设计研究者
大语言模型的兴起引发了对人工智能驱动硬件设计的关注，核心问题在于：在智能体时代，高阶综合（HLS）是否仍具价值？本文认为，HLS依然至关重要。尽管成熟的智能体硬件系统将结合HLS与RTL，但本文聚焦于HLS在实现智能体优化中的作用。HLS具备更快的迭代周期、可移植性和设计可变性，使其成为智能体优化的理想层级。本文提出三点贡献：第一，阐明HLS作为实用抽象层和智能体硬件设计基准的重要性；第二，识别当前HLS工具的关键局限——性能反馈不足、接口僵化、调试困难，而这些正是智能体可解决的问题；第三，提出智能体与HLS共生演化的分类框架，明确责任如何从人类设计师逐步转移至自主设计伙伴。
原文摘要 · Abstract (English)
The rise of large language models has sparked interest in AI-driven hardware design, raising the question: does high-level synthesis (HLS) still matter in the agentic era? We argue that HLS remains essential. While we expect mature agentic hardware systems to leverage both HLS and RTL, this paper focuses on HLS and its role in enabling agentic optimization. HLS offers faster iteration cycles, portability, and design permutability that make it a natural layer for agentic optimization. This position paper makes three contributions. First, we explain why HLS serves as a practical abstraction layer and a golden reference for agentic hardware design. Second, we identify key limitations of current HLS tools, namely inadequate performance feedback, rigid interfaces, and limited debuggability that agents are uniquely positioned to address. Third, we propose a taxonomy for the symbiotic evolution of agentic HLS, clarifying how responsibility shifts from human designers to AI agents as systems advance from copilots to autonomous design partners.
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