AutoSLIDE:自动源级仪器和调试的HLS

Liwei Yang, S. Gurumani, Deming Chen, K. Rupnow
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引用次数: 10

摘要

高级综合(HLS)工具的结果质量得到了改进,这使得它们在硬件设计中得到了越来越多的采用。然而,hls生产的设计的功能验证仍然是一个主要挑战。一旦漏洞暴露,设计人员必须回溯数千个信号和模拟周期,以确定潜在的原因。hls生成的非人类可读RTL进一步加剧了这一挑战。在本文中,我们提出了AutoSLIDE,这是一个自动的跨层验证框架,用于检测关键操作,检测软件和硬件执行之间的差异,并跟踪可疑的数据路径树以识别检测到的差异的错误来源。AutoSLIDE还维护RTL数据路径操作、LLVM-IR操作和C/ c++源代码之间的映射,以精确地将错误的根源定位到源代码中的确切行/操作,从而大大减少用户定位错误的工作量。我们通过检测和定位以前版本的CHStone基准套件中的错误来证明其有效性。此外,我们展示了AutoSLIDE的效率,与详尽的仪器相比,它在HLS时间上的开销很低(27%),软件跟踪收集(10%),并且显着减少了跟踪大小和模拟时间。
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AutoSLIDE: Automatic Source-Level Instrumentation and Debugging for HLS
Improved quality of results from high level synthesis (HLS) tools have led to their increased adoption in hardware design. However, functional verification of HLS-produced designs remains a major challenge. Once a bug is exposed, designers must backtrace thousands of signals and simulation cycles to determine the underlying cause. The challenge is further exacerbated with HLS-produced non-human-readable RTL. In this paper, we present AutoSLIDE, an automated cross-layer verification framework that instruments critical operations, detects discrepancies between software and hardware execution, and traces the suspect datapath tree to identify bug source for the detected discrepancy. AutoSLIDE also maintains mappings between RTL datapath operations, LLVM-IR operations, and C/C++ source code to precisely pinpoint the root-cause of bugs to the exact line/operation in source code, substantially reducing user effort to localize bugs. We demonstrate the effectiveness by detecting and localizing bugs from former versions of the CHStone benchmark suite. Furthermore, we demonstrate the efficiency of AutoSLIDE, with low overhead in HLS time (27%), software trace gathering (10%), and significantly reduced trace size and simulation time compared to exhaustive instrumentation.
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