REAPR: Reconfigurable engine for automata processing
REAPR: Reconfigurable engine for automata processing
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REAPR：用于自动机处理的可重构引擎
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Mircea R. Stan
中科院分区：
文献类型：
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作者：
Ted Xie;V. Dang;J. Wadden;K. Skadron;Mircea R. Stan
有限自动机已经在从网络安全到机器学习等引人注目的领域证明了它们的有用性。虽然之前的工作主要集中在它们对纯正则表达式工作负载（如防病毒和网络安全规则集）的适用性上，但最近的研究表明，自动机可以优化其他领域（如机器学习甚至粒子物理）的算法性能。不幸的是，它们在传统CPU架构上的模拟从根本上来说很慢，并且受到内存的进一步瓶颈。在本文中，我们提出了REAPR: Reconfigurable Engine for Automata PRocessing，这是一个灵活的框架，综合了用于自动机处理应用程序的RTL以及用于处理内核之间和内核之间数据传输的I/O。我们表明，即使有内存和控制流开销，与其他架构相比，fpga仍然能够实现极高吞吐量的自动工作负载计算。
Finite automata have proven their usefulness in high-profile domains ranging from network security to machine learning. While prior work focused on their applicability for purely regular expression workloads such as antivirus and network security rulesets, recent research has shown that automata can optimize the performance for algorithms in other areas such as machine learning and even particle physics. Unfortunately, their emulation on traditional CPU architectures is fundamentally slow and further bottlenecked by memory. In this paper, we present REAPR: Reconfigurable Engine for Automata PRocessing, a flexible framework that synthesizes RTL for automata processing applications as well as I/O to handle data transfer to and from the kernel. We show that even with memory and control flow overheads, FPGAs still enable extremely high-throughput computation of automata workloads compared to other architectures.