arXiv:2512.00016cs.ARcs.AI2025-12被引 1

用智能体+工程师协作,自动设计验证处理器,省时省力。

Architect in the Loop Agentic Hardware Design and Verification

  • 构建智能体分步拆解设计,生成HDL和测试代码,工程师参与调试与合成。
  • 成功实现两个处理器(类LEGv8和类RISC-V)在DE-10 Lite FPGA上验证与综合。
  • 每处理器约需百万推理令牌,无需专用硬件,适合快速原型与扩展实验。

硬件设计复杂度持续上升,亟需更高效的设计与验证方法。尽管生成式AI已用于小规模组件的HDL与测试平台生成,但全流程处理器设计自动化仍鲜见。本文提出一种‘人在环中’的智能体驱动处理器设计与验证方法,遵循层次化、模块化设计原则。智能体在可选规格指导下分解任务,生成HDL与cocotb测试,并在调试与综合阶段引入工程师干预。我们通过该方法设计了多个数字系统,本文以两个简单处理器为例:第一个是类LEGv8处理器,在DE-10 Lite FPGA上完成验证、综合与编程;第二个是类RISC-V的32位处理器,完成设计与验证并综合,但未编程至FPGA。整个过程平均使用约一 million 推理令牌,结合推理型模型(如gemini-pro)与非推理型模型(如gpt-5-mini),根据任务复杂度动态选择。结果表明,该方法可在无专用硬件条件下低成本开展硬件设计实验,具备良好可扩展性,未来将探索片上系统(SoC)应用。

原文摘要 · Abstract (English)

The ever increasing complexity of the hardware design process demands improved hardware design and verification methodologies. With the advent of generative AI various attempts have been made to automate parts of the design and verification process. Large language models (LLMs) as well as specialized models generate hdl and testbenches for small components, having a few leaf level components. However, there are only a few attempts to automate the entire processor design process. Hardware design demands hierarchical and modular design processes. We utilized this best practice systematically and effectively. We propose agentic automated processor design and verification with engineers in the loop. The agent with optional specification tries to break down the design into sub-components, generate HDL and cocotb tests, and verifies the components involving engineer guidance, especially during debugging and synthesis. We designed various digital systems using this approach. However, we selected two simple processors for demonstration purposes in this work. The first one is a LEGv8 like a simple processor verified, synthesized and programmed for the DE-10 Lite FPGA. The second one is a RISC-V like 32-bit processor designed and verified in similar manner and synthesized. However, it is not programmed into the DE-10 Lite. This process is accomplished usually using around a million inference tokens per processor, using a combination of reasoning (e.g gemini-pro) and non-reasoning models (eg. gpt-5-mini) based on the complexity of the task. This indicates that hardware design and verification experimentation can be done cost effectively without using any specialized hardware. The approach is scalable, we even attempted system-on-chip, which we want to experiment in our future work.

硬件设计智能体自动化FPGA

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