While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at main-memory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.

中文翻译：

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FPGA上的内存数据库加速：一项调查

尽管FPGA已经在数据库系统中得到了先期使用，但是由于以下三个原因，近年来，业界和学术界对使用FPGA来加速数据库的兴趣都在下降。首先，专门针对内存数据库，与常规I / O集成的FPGA提供了不足的带宽，从而限制了性能。其次，GPU还可以提供高吞吐量，并且易于编程，已经成为强大的加速器替代方案。第三，对FPGA进行编程要求开发人员具备从高级算法设计到底层电路实现的全栈技能。好消息是这些挑战正在得到解决。新的接口技术以主内存带宽将FPGA连接到系统中，最新的FPGA提供与GPU相比在容量和带宽方面具有竞争力的本地存储器。通过支持主机和加速器之间的共享一致性虚拟内存，对高级语言的支持以及特定于域的工具来自动生成FPGA设计，编程的便利性得到了改善。因此，本文对使用FPGA加速内存数据库系统进行了调查，这些系统针对的设计可以以主存储器的速度运行。