Reconfigurable computing can significantly improve the performance and energy efficiency of many applications. However, FPGA-based chips are evolving rapidly, increasing the difficulty of evaluating the impact of new capabilities such as HBM and high-speed links. In this paper, a real-world application was implemented on different FPGAs in order to better understand the new capabilities of modern FPGAs and how new FPGA technology improves performance and scalability. The aforementioned application was the preconditioned conjugate gradient (PCG) method that is utilized in underground analysis. The implementation was done on four different FPGAs, including an MPSoC, taking into account each platform’s characteristics. The results show that today’s FPGA-based chips offer eight times better performance on a memory-bound problem than 5-year-old FPGAs, as they incorporate HBM and can operate at higher clock frequencies.

中文翻译：

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基于 FPGA 的平台上的预条件共轭梯度加速

可重构计算可以显着提高许多应用程序的性能和能源效率。然而，基于 FPGA 的芯片正在迅速发展，增加了评估 HBM 和高速链路等新功能的影响的难度。在本文中，为了更好地了解现代 FPGA 的新功能以及新的 FPGA 技术如何提高性能和可扩展性，在不同的 FPGA 上实现了一个真实的应用程序。上述应用是用于地下分析的预处理共轭梯度 (PCG) 方法。该实施是在四个不同的 FPGA 上完成的，包括一个 MPSoC，同时考虑了每个平台的特性。结果表明，今天基于 FPGA 的芯片在内存受限问题上的性能比使用 5 年的 FPGA 好八倍，